• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Journal of Christian Education in Korea
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• v.72
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• pp.49-70
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• 2022

The purpose of this study is to suggest a response and solution through Christian educational care to the crisis and change of the era of low birth rate faced by Korean society and the Korean church. This study proposes to find an alternative to the biblical aspect of pregnancy, childbirth, and parenthood as God's blessing for the demographic cliff and low birth rate problem that have become a reality in Korean society and churches. Being a parent in an age of low birth rate is very difficult, but on the other hand, it gives happiness and joy. Being a parent is a blessing from God, and is the most important and valuable thing in life. However, modern society emphasizes the right and necessity to choose one's own parenthood status. In the nuclear family, the decrease in the number of children, and the development of child research, parents feel more responsibility and economic burden for raising children than ever before. Therefore, it is a reality that the number of people who delay becoming parents or voluntarily do not have children is gradually increasing. To improve the perception of becoming a parent due to a decrease in responsibility for raising children, it is necessary to shed light on marriage, pregnancy, childbirth, and childrearing from a Christian educational point of view. In addition, it is necessary to understand the recognition of being a parent and the characteristics of childbirth and rearing, and to analyze past and present value changes. This study will also discuss the causes of low birthrate and try to provide Christian educational care for childcare including solving the low birthrate problem.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.805-808
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• 2008

In this paper, it was performed the analysis on various intersection acoustic patterns for detection rate improvement of accident sound detection system : an acoustic pattern analysis on general traffic noise, an acoustic pattern analysis on engine noise, an acoustic pattern analysis on obstruct factors for accident sound detection system. There are remarkable differences between the acoustic patterns of traffic noise and accident sound, and we most consider the acoustic patterns when we compose the accident traffic detection system by acoustic because there is error range of 20[dB] according to the volume of traffic in intersection.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.173-179
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• 2010

Over recent years, much research attention has been devoted to a two-factor authentication mechanism which integrates both tokenized pseudorandom numbers with user specific biometric features for biometric verification, known as Biohash. The main advantage of Biohash over sole biometrics is that Biohash is able to achieve a zero equal error rate and provide a clean separation of the genuine and imposter populations, thereby allowing elimination of false accept rates without imperiling the false reject rates. Nonetheless, when the token of a user is compromised, the recognition performance of a biometric system drops drastically. As such, a few solutions have been proposed to improve the degraded performance but such improvements appear to be insignificant. In this paper, we investigate and pinpoint the basis of such deterioration. Subsequently, we propose a two-level approach by utilizing strong inner products and fuzzy logic weighting strategies accordingly to increase the original performance of Biohash under this scenario.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.227-232
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• 2013

BCI (Brain Computer Interface) is technology to control external devices by measuring the brain activity, such as electroencephalogram (EEG), so that handicapped people communicate with environment physically using the technology. Among them, EEG is widely used in various fields, especially robot agent control by using several signal response characteristics, such as P300, SSVEP (Steady-State Visually Evoked Potential) and motor imagery. However, in order to control the robot agent without any constraint and precisely, it should take advantage of not only a signal response characteristic, but also combination. In this paper, we try to use the fusion of motor imagery and P300 from EEG for practical use of robot control in BCI environment. The results of experiments are confirmed that the recognition rate decreases compared with the case of using one kind of features, whereas it is able to classify each both characteristics and the practical use technology based on mobile robot and wireless BCI measurement system is implemented.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.195-203
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• 2012

This paper proposes ground target classification algorithm based on decision fusion and feature extraction method using multi-sensor images. The decisions obtained from the individual classifiers are fused by applying a weighted voting method to improve target recognition rate. For classifying the targets belong to the individual sensors images, features robust to scale and rotation are extracted using the difference of brightness of CM images obtained from CCD image and the boundary similarity and the width ratio between the vehicle body and turret of target in FLIR image. Finally, we verity the performance of proposed ground target classification algorithm and feature extraction method by the experimentation.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.139-147
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• 2013

This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

• Journal of Korean Clinical Nursing Research
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• v.20 no.3
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• pp.406-416
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• 2014

Purpose: Screening of delirium using delirium assessment tools could promote delirium detection, however, there is lack of report about regular delirium assessment in Korea. This study was intended to describe the prevalence and related risk factors of delirium in intensive care unit (ICU). Methods: The Confusion Assessment Method for the ICU (CAM-ICU) data which were evaluated by nurses in ICUs was obtained through retrospective chart review. Data were analyzed using descriptive statistics, Chi-square test, t-test, Mann-Whitney U test, and stepwise logistic regression. Results: Delirium was evaluated in 125 patients. The incidence rate of delirium was 27.2% with a high prevalence of hypoactive delirium compared to hyperactive delirium (61.8 vs. 38.2%). Those with delirium were older, had hypertension, stayed longer in hospital, receiving ventilator support, had more number of catheters, had low serum protein and albumin level. Delirium incidence also varied according to diagnosis. Age, diagnosis of gastrointestinal disease, and application of ventilator were the significant risk factors for the incidence of delirium. Conclusion: Routine delirium screening is important for early detection of delirium. Identification of high-risk group and running delirium prevention programs could improve early recognition of delirium in ICU.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.589-592
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• 1997

In this paper, we propose a simple algorithm to calculate the number of passing cars at night by using an image processing sensor for digital black and white images with 256 tone levels. To recognize cars, we capture their head lamps. The reflection of the head lamps is one of the most troublesome factors in recognizing cars. The main problem in this paper is how to recognize cars under the influence of the reflection of the head lamps especially in rainy days. In general, the image of a head lamp is nearly circular and the reflection is long and narrow. On the difference of these forms, we can exclude the reflection in our proposed algorithms For real-time operation and simple calculation, we recognize the existence of cars using fifteen lines with 256 tone levels. In the experimental application on a road, the recognition rate of a real-time operation is more than 90%. Moreover, we will also explain briefly how to recognize passing cars for 24 hours.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.875-878
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• 2004

Researching in text detection and segmentation has been done for a long period in the OCR area. However, there is some other area that the text detection and segmentation from images can be very useful. In this report, we first propose the design of a mobile translator system which helps non-native speakers to understand the foreign language using ubiquitous mobile network and camera mobile phones. The main focus of the paper will be the algorithm in detecting and segmenting texts embedded in the natural scenes from taken images. The image, which is captured by a camera mobile phone, is transmitted to a translator server. It is initially passed through some preprocessing processes to smooth the image as well as suppress noises. A threshold is applied to binarize the image. Afterward, an edge detection algorithm and connected component analysis are performed on the filtered image to find edges and segment the components in the image. Finally, the pre-defined layout relation constraints are utilized in order to decide which components likely to be texts in the image. A preliminary experiment was done and the system yielded a recognition rate of 94.44% on a set of 36 various natural scene images that contain texts.