• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.898-902
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• 2015

Alternative radio-navigation technologies aim at providing continuous navigation solution even if one cannot use GNSS (Global Navigation Satellite System). In shadowing region such as indoor environment, GNSS signal is no longer available and the alternative navigation system should be used together with GNSS to provide seamless positioning. For soldiers in battlefield where GNSS signal is jammed or in street battle, the alternative navigation system should work without positioning infrastructure. Moreover, the radio-navigation system should have scalability as well as high accuracy performance. This paper presents a TWR (Two-Way-Ranging)-based cooperative positioning system (CPS) that does not require location infrastructure. It is assumed that some members of CPS can obtain GNSS-based position and they are called mobile anchors. Other members unable to receive GNSS signal compute their position using TWR measurements with mobile anchors and neighboring members. Error propagation in CPS is analytically studied in this paper. Error budget for TWR measurements is modeled first. Next, location error propagation in CPS is derived in terms of range errors. To represent the location error propagation in the CPS, Location Error Propagation Indicator (LEPI) is proposed in this paper. Simulation results show that location error of tags in CPS is mainly influenced by the number of hops from anchors to the tag to be positioned as well as the network geometry of CPS.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.599-607
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• 2018

The purpose of this study was to identify the influence of job esteem and empathy on customer orientation of college nursing students in the convergence society. The participants were 213 college nursing students, data were collected between July to August, 2018. Data were analyzed using t-test, ANONA, Pearson Correlation, Multiple regression. The factors influencing customer orientation were empathy, job esteem, communication with patients, and perception of customer orientation. These factors explained 32.0% of variance in customer orientation. Therefore, in order to improve the customer orientation of nursing students, education courses and intervention for improving cognitive empathy and finding ways to improve job esteem are needed.

• Anxiety and mood
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• v.10 no.2
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• pp.143-150
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• 2014

Objective : The subgenual cingulate cortex, a part of default-mode network, has been known to playa key role in the pathophysiology of depression. The previous studies have reported abnormal functional connectivity between the subgenual cingulate cortex and other brain regions in the patients with depression. The goal of this shldy was to explore the resting-state functional connectivity of the subgenual cingulate cortex between the patients with depression and healthy subjects. Methods : Twenty patients with major depression and age- and sex-matched 20 healthy subjects underwent 5-minute resting state fMRI scans. The functional connectivity map in each subject was acquired using seed-based correlation analysis with the seed located in the subgenual cingulate cortex (Talairach coordinates; x=-10, y=5, z=-10). The functional connectivity maps were calculated using AFNI and compared between the patient and healthy subject group via two-sample T-test using 3dttest++ in AFNI package. Results : Functional connectivity was decreased between the subgenual cingulate cortex and both sides of fusiform gyrus in depressed subjects. Connectivity was also decreased between the subgenual cingulate cortex and the left cerebellum in the patient group. There was no correlation between the severity of depression and the degree of functional connectivity between the subgenual cingulate cortex and the regions showing decreased functional connectivity. Conclusion : Decreased resting-state functional connectivity between the subgenual cingulate cortex and both sides of fusiform gyrus, and decreased connectivity between the subgenual cingulate cortex and the left cerebellum found in the patients with major depression in comparison to the healthy subjects might be related to abnormal emotional and cognitive processing of depressed patients.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.304-310
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• 2010

Chronic pain and herniated disk is a common disease that 80% of adults are experienced. There diseases rates of caused by the physical shock, such as the traffic accident, and the accidental fall is about 10%. And the most of these diseases is caused by having habitual incorrect position. People know that incorrect position would cause to accumulate continuous stress, but it is not easy to correct position. Because it does not recognize incorrect position repeated habitual consequently. This system collects data of user position after sensors that could measure position attach on use and presumes correct position used by position presumption algorithms. Its system purpose is continuing incorrect position could be aware to user and lead to change to correct position to prevent habituation of incorrect position. If habitual of correct position continues through accurate measurement and repeating cognitive learning, it would help for children and chronic patience.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1494-1505
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• 2010

A variety of sounds take place in medium and small-sized manufactories producing many kinds of parts in a small quantity with one press. We developed the identification system for the quality of parts using HTM(Hierarchical Temporal Memory)-based sound recognition. HTM is the theory that the operation principle of human brain's neocortex is applied to computer, suggested by Jeff Hopkins. This theory memorizes temporal and spatial patterns hierarchically about the real world, which is known for its cognitive power superior to the previous recognition technologies in many cases. By applying the HTM model to the sound recognition, we developed the identification system for the quality of molding parts. In order to verify its performance we recorded the various sounds at the moment of producing parts in the real factory, constructed the HTM network of sound, and then identified the quality of parts by repeating learning and training. It reveals that this system gets an excellent and accurate results at the noisy factory.

• Journal of Digital Convergence
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• v.12 no.10
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• pp.211-222
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• 2014

This study investigates the antecedents of continuance intention of mobile SNS. In addition, it also explores the mediating role of perceived value in the relationship between the antecedent and continuance intention. This study posits that a social influence, perceived usefulness, privacy concern, and a perceived effort of use are major factors to influence continuance intention of SNS. By using a PLS analysis, this finding suggests that consumers' perception of the value of SNS which is previously overlooked, is a primary determinant of continuance intention, and the other exploratory factors are mediated through perceived value in cognitive aspect. This research aims to examine consumer's SNS continuance intention in terms of consumer's perspective, not just from the technology user perspective. Therefore, this research provides a useful guideline for marketing managers on how to manage Mobile SNS properly.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.3
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• pp.55-70
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• 2016

The most critical research issue in MANET environment is on supporting reliable communication between various devices. Various Multi-Hop Routing Protocol studies have proceeded. However, some problems you might have found when you use the existing link state routing technique are that it increases Control Message Overhead and it is unstable when node moves in CR circumstance which has transformation of using channel and MIMC circumstance which uses a number of interfaces. This essay offers a technique which is based on On-Demand Hello and the other technique which used Broadcast Interface of optimization as a solution to decrease Control Message Overhead. Also it proposes Quick Route Restoration technique which is utilized by GPS and MPR Selection technique which consider mobility as a solution of stable communication when node moves. Those offered Routing Protocol and OPNET based simulator result will be expected to be an excellent comparison in related research fields.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.441-452
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• 2021

In this paper, we propose a method for generating luma signal mapping function to improve the coding efficiency of luma signal mapping methods in LMCS. In this paper, we propose a method to reflect the cognitive and perceptual features by multiplying the attention map of convolutional neural networks on local spatial variance used to reflect local features in the existing LMCS. To evaluate the performance of the proposed method, BD-rate is compared with VTM-12.0 using classes A1, A2, B, C and D of MPEG standard test sequences under AI (All Intra) conditions. As a result of experiments, the proposed method in this paper shows improvement in performance the average of -0.07% for luma components in terms of BD-rate performance compared to VTM-12.0 and encoding/decoding time is almost the same.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.