• Journal of Life Science
• /
• v.17 no.12
• /
• pp.1717-1722
• /
• 2007

In the present study, all of the treadmill exercise-trained SHR expressed clear adaptive changes such as reduced resting heart rate and blood pressures, LPOA, homocysteine Therefore, treadmill exercise was sufficient to induce physiological adaptation in the SHR. Endurance training is known to induce physiological cardiac hypertrophy, while hypertension induces patho logical cardiac hypertrophy that increases cardiomyocyte apoptosis. The pathological adaptation to pressure overload has also been associated with a further increase in the expression of several marker genes including cardiomyocyte ET-1 in the SHR, but not in the exercise-trained SHR. Additionally, there is an increase in the endothelial nitricoxide synthases (eNOS) protein expression of soleus, gastrocnemius, and extensor digitorum longus muscle in the exercise-trained SHR but not in the SHR in the present study. Thus, compared to pathological adaptation to pressure overload, physiological adaptation to exercise training is associated with distinct alterations in cardiac and molecular phenotypes. based on these results, exercise training improves hypertension by cardiovascular regulating genes and hemodynamic parameters.

• Journal of Internet Computing and Services
• /
• v.4 no.4
• /
• pp.9-14
• /
• 2003

A GUI (graphical user interface) has been a dominant platform for HCI (human computer interaction). A GUI - based interaction has made computers simpler and easier to use. The GUI - based interaction, however, does not easily support the range of interaction necessary to meet users' needs that are natural. intuitive, and adaptive. In this paper, the modified BMA (block matching algorithm) is proposed to track a hand in a sequence of an image and to recognize it in each video frame in order to replace a mouse with a pointing device for a virtual reality. The HCI system with 30 frames per second is realized in this paper. The modified BMA is proposed to estimate a position of the hand and segmentation with an orientation of motion and a color distribution of the hand region for real - time processing. The experimental result shows that the modified BMA with the YCbCr (luminance Y, component blue, component red) color coordinate guarantees the real - time processing and the recognition rate. The hand tracking by the modified BMA can be applied to a virtual reclity or a game or an HCI system for the disable.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.8
• /
• pp.4092-4104
• /
• 2017

In this paper, an efficient 4K and 8K UHD(Ultra High Definition) transmission scheme is proposed on the convergence networks with broadcasting and LTE(Long Term Evolution) by using CoMP(Coordinated Multi-Point). A video data is compressed and divided into BL(Base Layer), E(Enhanced layer)1, E2 and E3 by scalable HEVC(High Efficiency Video Coding). The divided layers can be combined by the scalable HEVC such as mobile HD, full HD, 4K and 8K UHD(Ultra High Definition). The divided layers are transmitted through the convergence networks with DVB-T2(Digital Video Broadcasting-$2^{nd}$ Generation Terrestrial) broadcasting system and LTE CoMP. This scheme transmits mobile HD and full HD layers through DVB-T2 broadcasting system by using M-PLP(Multiple-physical Layer Pipes), and adaptively transmits 4K or 8K UHD layer through LTE CoMP with MMT(MPEG Media Transport) server. An adaptive transmitting and receiving scheme in the LTE CoMP system provides 4K or 8K UHD layer to a user according to the user status. The proposed scheme is verified by showing the system-level simulation results which is better BER(bit-error-rate) performance than the conventional scheme. The results show that the proposed scheme provides the stable video contents to the user especially at the cell edge.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.10
• /
• pp.955-961
• /
• 2010

In this paper, we describe a study of a vehicle detection method based on a Boosting Classifier which uses Histogram of Oriented Gradient (HOG) features and Image Segmentation techniques. An input image is segmented by means of a split and merge algorithm. Then, the two largest segmented regions are removed in order to reduce the search region and speed up processing time. The HOG features are then calculated for each pixel in the search region. In order to detect the vehicle region we used the AdaBoost (adaptive boost) method, which is well known for classifying samples with two classes. To evaluate the performance of the proposed method, 537 training images were used to train and learn the classifier, followed by 500 non-training images to provide the recognition rate. From these experiments we were able to detect the proper image 98.34% of the time for the 500 non-training images. In conclusion, the proposed method can be used for detecting the location of a vehicle in an intelligent vehicle control system.

• Research in Community and Public Health Nursing
• /
• v.32 no.3
• /
• pp.254-267
• /
• 2021

Purpose: The purpose of this study was to identify the effects of brain-wave biofeedback training nursing intervention (NFT) upon enhancing self-regulation response in adolescence with emotional behavior problems in school. Methods: A quasi-experimental design was conducted. The participants were assigned to the experimental group (n=24) or the control group (n=24). The experimental group additionally received NFT. The NFT was conducted 10 sessions for 30 minutes per session with the band reward and inhibit training which matched their Quantitative Electroencephalography (QEEG), participant's demand and chief complaint. Data were collected with QEEG and heart rate variability (HRV) in physiological response, self-efficacy in cognitive response, depression in emotional response, impulsivity and delay gratification in behavioral response of self-regulation. Results: The general characteristics and the pre-test scores of two groups were all homogeneous. The experimental group was reported to be significantly higher in QEEG homeostasis, HRV homeostasis, self-efficacy, and delay gratification than the control group. The experimental group was reported to be significantly lower in depression and impulsivity. Conclusion: The results indicate that NFT using brain cognitive neuroscience approach is effective in enhancing self-regulation response. Therefore, this nursing intervention using brain cognitive neuroscience approach can be applied as an effective self-regulation nursing intervention for adolescents with emotional behavior problems in communities for adaptive life.

• Journal of Broadcast Engineering
• /
• v.26 no.2
• /
• pp.143-154
• /
• 2021

In this paper, we propose a method of compressing digital hologram standardized data provided by JPEG Pleno. In numerical reconstruction of digital holograms, the addition of random phases for visualization reduces speckle noise due to interference and doubles the compression efficiency of holograms. Holograms are composed of completely complex floating point data, and due to ultra-high resolution and speckle noise, it is essential to develop a compression technology tailored to the characteristics of the hologram. First, frequency characteristics of hologram data are analyzed using various wavelet filters to analyze energy concentration according to filter types. Second, we introduce the subband selection algorithm using energy concentration. Finally, the JPEG2000, SPIHT, H.264 results using the Daubechies 9/7 wavelet filter of JPEG2000 and the proposed method are used to compress and restore, and the efficiency is analyzed through quantitative quality evaluation compared to the compression rate.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.2
• /
• pp.714-720
• /
• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Journal of the Institute of Convergence Signal Processing
• /
• v.21 no.4
• /
• pp.202-208
• /
• 2020

Recently, the importance of non-face-to-face has been emphasized due to COVID-19, and the use of sharing spaces is also expanding. The use of uncontact check-in technology for access control of sharing spaces reduces waiting time and optimizes workers' efficiency, resulting in operational cost savings. In this paper, we propose a sharing space access management system based on a mobile key and RCU (Room Control Unit), access to the facility using a mobile key, and monitor the facility using an RCU. Proposal system is for shared accommodation, rental field (residence, sale-selling hotel), shared office, etc. when there is a one-time visitor on a specific day and time, the corresponding password is delivered to the mobile platform to expose and key the existing password. It is supported by a field-adaptive system that can reduce discomfort such as delivery. In order to test the operation of the proposed integrated system, tests were conducted according to scenarios to understand the overall status of the user's reservation, check-in, and check-out, and a 100% success rate was derived for each item by setting performance indicators to prove test reliability.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.3
• /
• pp.113-123
• /
• 2022

Ray tracing-based rendering creates by far more realistic images than the traditional rasterization-based rendering. However, it is still burdensome when implemented for a Head-Mounted Display (HMD) system that demands a wide field of view and a high display refresh rate. Furthermore, for presenting high-quality images on the HMD screen, a sufficient number of ray sampling should be carried out per pixel to alleviate visually annoying spatial and temporal aliases. In this paper, we extend the recent selective foveated ray tracing technique by Kim et al. [1], and propose an improved real-time rendering technique that realizes the rendering effect of the classic Whitted-style ray tracing on the HMD system. In particular, by combining the ray tracing hardware-based acceleration technique and time-constrained rendering scheme, we show that fast HMD ray tracing is possible that is well suited to human visual systems.

• The Journal of Society for e-Business Studies
• /
• v.27 no.1
• /
• pp.1-13
• /
• 2022

With the development of network technologies, the security to protect organizational resources from internal and external intrusions and threats becomes more important. Therefore in recent years, the anomaly detection algorithm that detects and prevents security threats with respect to various security log events has been actively studied. Security anomaly detection algorithms that have been developed based on rule-based or statistical learning in the past are gradually evolving into modeling based on machine learning and deep learning. In this study, we propose a deep-autoencoder model that transforms LSTM-autoencoder as an optimal algorithm to detect insider threats in advance using various machine learning analysis methodologies. This study has academic significance in that it improved the possibility of adaptive security through the development of an anomaly detection algorithm based on unsupervised learning, and reduced the false positive rate compared to the existing algorithm through supervised true positive labeling.