• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.267-275
• /
• 2021

Smart application is developed in this paper by using an android-based platform to automatically determine the human emergency state (Lifesaver) by using different technology sensors of the mobile. In practice, this Lifesaver has many applications, and it can be easily combined with other applications as well to determine the emergency of humans. For example, if an old human falls due to some medical reasons, then this application is automatically determining the human state and then calls a person from this emergency contact list. Moreover, if the car accidentally crashes due to an accident, then the Lifesaver application is also helping to call a person who is on the emergency contact list to save human life. Therefore, the main objective of this project is to develop an application that can save human life. As a result, the proposed Lifesaver application is utilized to assist the person to get immediate attention in case of absence of help in four different situations. To develop the Lifesaver system, the GPS is also integrated to get the exact location of a human in case of emergency. Moreover, the emergency list of friends and authorities is also maintained to develop this application. To test and evaluate the Lifesaver system, the 50 different human data are collected with different age groups in the range of (40-70) and the performance of the Lifesaver application is also evaluated and compared with other state-of-the-art applications. On average, the Lifesaver system is achieved 95.5% detection accuracy and the value of 91.5 based on emergency index metric, which is outperformed compared to other applications in this domain.

• 한국HCI학회:학술대회논문집
• /
• 2006.02a
• /
• pp.666-671
• /
• 2006

'Multi modal 인터페이스'란 인간과 기계의 통신을 위해 음성, 키보드, 펜을 이용, 인터페이스를 하는 방법을 말한다. 최근 들어 많은 휴대용 단말기가 보급 되고, 단말기가 소형화, 지능화 되어가고, 단말기의 어플리케이션도 다양해짐에 따라 사용자가 보다 편리하고 쉽게 사용할 수 있는 입력 방법에 기대치가 높아가고 있다. 현재 휴대용 단말기에 가능한 입력장치는 단지 단말기의 버튼이나 터치 패드(PDA 경우)이다. 하지만 장애인의 경우 버튼이나 터치 패드를 사용하기 어렵고, 휴대용 단말기로 게임을 하는데 있어서도, 어려움이 많으며 새로운 게임이나 어플리케이션 개발에도 많은 장애요인이 되고 있다. 이런 문제점들은 극복하기 위하여, 본 논문에서는 휴대용 단말기의 새로운 Multi Modal 인터페이스를 제시 하였다. PDA(Personal Digital Assistants)를 이용하여 더 낳은 재미와 실감을 줄 수 있는 Multi Modal 인터페이스를 개발하였다. 센서를 이용하여 휴대용 단말기를 손목으로 제어를 가능하게 함으로서, 사용자에게 편리하고 색다른 입력 장치를 제공 하였다. 향후 음성 인식 기능이 추가 된다면, 인간과 인간 사이의 통신은 음성과 제스처를 이용하듯이 기계에서는 전통적으로 키보드 나 버튼을 사용하지 않고 인간처럼 음성과 제스처를 통해 통신할 수 있을 것이다. 또한 여기에 진동자를 이용하여 촉감을 부여함으로써, 그 동안 멀티 모달 인터페이스에 소외된 시각 장애인, 노약자들에게도 정보를 제공할 수 있다. 실제로 사람은 시각이나 청각보다 촉각에 훨씬 빠르게 반응한다. 이 시스템을 게임을 하는 사용자한테 적용한다면, 능동적으로 게임참여 함으로서 좀더 실감나는 재미를 제공할 수 있다. 특수한 상황에서는 은밀한 정보를 제공할 수 있으며, 앞으로 개발될 모바일 응용 서비스에 사용될 수 있다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.1
• /
• pp.108-114
• /
• 2012

We propose a dispersive finite-difference time domain(FDTD) algorithm suitable for the electromagnetic analysis of the human body. In this work, the dispersion relation of the human body is modeled by a quadratic complex rational function(QCRF), which leads to an accurate and efficient FDTD algorithm. Coefficients(involved in QCRF) for various human tissues are extracted by applying a weighted least square method(WLSM), referred to as the complex-curve fitting technique. We also presents the FDTD formulation for the QCRF-based dispersive model in detail. The QCRFbased dispersive model is significantly accurate and its FDTD implementation is more efficient than the counterpart of the Cole-Cole model. Numerical examples are used to show the validity of the proposed FDTD algorithm.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.06d
• /
• pp.204-207
• /
• 2007

최근 대두되고 있는 무선 센서네트워크는 실생활의 많은 부분에 있어 그 응용 분야를 넓혀 가고 있다. 본 연구는 WSN의 응용 중 Human Health Care에 주안을 두어 WSN을 이용한 원격 진료 시스템에 대해 설계 및 구현을 하였다. 원격 진료 시스템을 위해 각 센서 노드들은 인체의 Body 정보를 수집할 수 있는 센서들을 가지고 신체의 각 부위에 부착된다. 또한 각 센서 노드들은 고유의 Human Body Code를 가지고 있으며 이 고유의 Code에 의해 인체의 어느 부위에서 측정된 Data인지를 Sink 노드로 전송하게 된다. Sink 노드는 수집된 정보를 원격에 위치한 의료진들에게 전송하며 원격의 의료진들은 Sink 노드에서 전송된 정보를 바탕으로 진료 정보를 환자 및 User에게 Feedback하게 된다. Human Body Code는 인체를 세분화하고 각 세분화한 신체 부위에 계층적으로 고유의 Code를 부여한다. 본 연구에서는 실제 Human Body Code를 직접 제작한 센서 Node에 주입하여 Human Body Network을 구성하여 인체에서 센싱되는 Data를 원격에 위치한 PC에서 진료 가능한 원격 진료 시스템을 구현하였다.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.1
• /
• pp.29-40
• /
• 2012

A challenging research issue that has been one of growing importance to those working in human-computer interaction are to endow a machine with an emotional intelligence. Thus, emotion recognition technology plays an important role in the research area of human-computer interaction, and it allows a more natural and more human-like communication between human and computer. In this paper, we propose the multimodal emotion recognition system using face and speech to improve recognition performance. The distance measurement of the face-based emotion recognition is calculated by 2D-PCA of MCS-LBP image and nearest neighbor classifier, and also the likelihood measurement is obtained by Gaussian mixture model algorithm based on pitch and mel-frequency cepstral coefficient features in speech-based emotion recognition. The individual matching scores obtained from face and speech are combined using a weighted-summation operation, and the fused-score is utilized to classify the human emotion. Through experimental results, the proposed method exhibits improved recognition accuracy of about 11.25% to 19.75% when compared to the most uni-modal approach. From these results, we confirmed that the proposed approach achieved a significant performance improvement and the proposed method was very effective.

• Journal of Internet Computing and Services
• /
• v.21 no.5
• /
• pp.21-29
• /
• 2020

Accurate estimation of human pose relies on backbone method in which its role is to extract feature map. Up to dated, the method of backbone feature extraction is conducted by the plain convolutional neural networks named by CNN and the residual neural networks named by Resnet, both of which have various architectures and performances. The CNN family network such as VGG which is well-known as a multiple stacked hidden layers architecture of deep learning methods, is base and simple while Resnet which is a bottleneck layers architecture yields fewer parameters and outperform. They have achieved inspired results as a backbone network in human pose estimation. However, they were used then followed by different pose estimation networks named by pose parsing module. Therefore, in this paper, we present a comparison between the plain CNN family network (VGG) and bottleneck network (Resnet) as a backbone method in the same pose parsing module. We investigate their performances such as number of parameters, loss score, precision and recall. We experiment them in the bottom-up method of human pose estimation system by adapted the pose parsing module of openpose. Our experimental results show that the backbone method using VGG network outperforms the Resent network with fewer parameter, lower loss score and higher accuracy of precision and recall.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.220-225
• /
• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.22 no.3
• /
• pp.191-208
• /
• 1997

Recently, self-efficacy is one of the critical constructs that have been found to influence human decisions about behavior selection and the performance associated with the selected behavior. The construct has been widely adaopted and tested in the fields of social psychology and/or other behavioral sciences. In information system field, however, it has been hardly studied, although computer self-efficiency could have been an important factor explaining and predicting human computer usage behaviors. From this perspective, main proposes of the study are : (1) to develop a measure of computer self-efficacy, 2) to identify the factors influencing self-efficacy, and 3) to reveal the relationship between self-efficacy and computer usage behavior. By reviewing the literature, past experience, others'use, encouragement by others, and anxiety are selected as the factors influencing computer self-efficacy. Four hypotheses concerning the relationship between each of the variables and computer self-efficacy are tested by LISREL. One more hypothesis about the relationship between computer self-efficacy and computer usage is also tested. The results show that computer self-efficacy is significantly influenced by computer ansiety, encouragement by others, and computer experience, and that it is closely correlated with computer usage behavior.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.5
• /
• pp.8-15
• /
• 2011

The goal of this paper is to describe how to construct an integrated simulation system that integrates limited perception-based mapping with spatial reasoning, path planning, and human motion style in order for a virtual soldier to effectively communicate with other virtual solders and/or human participants in a simulation. Virtual human research often ignores or simplifies perception by using a full map (with omniscient perception). In addition, previous research used a placement node where virtual environment designers save in advance the required information. However, this paper also shows that the human-like movement behavior can be achieved by the integrated ECA system with the mapping that supports a spatial understanding and does not require the omniscient perception.

• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.111-118
• /
• 2022

This research article presents the work that is related to the application of different machine learning based similarity techniques on religious text for identifying similarities and differences among its various translations. The dataset includes 10 different English translations of verses (Arabic: Ayah) of two Surahs (chapters) namely, Al-Humazah and An-Nasr. The quantitative similarity values for different translations for the same verse were calculated by using the cosine similarity and semantic similarity. The corpus went through two series of experiments: before pre-processing and after pre-processing. In order to determine the performance of machine learning based similarities, human annotated similarities between translations of two Surahs (chapters) namely Al-Humazah and An-Nasr were recorded to construct the ground truth. The average difference between the human annotated similarity and the cosine similarity for Surah (chapter) Al-Humazah was found to be 1.38 per verse (ayah) per pair of translation. After pre-processing, the average difference increased to 2.24. Moreover, the average difference between human annotated similarity and semantic similarity for Surah (chapter) Al-Humazah was found to be 0.09 per verse (Ayah) per pair of translation. After pre-processing, it increased to 0.78. For the Surah (chapter) An-Nasr, before preprocessing, the average difference between human annotated similarity and cosine similarity was found to be 1.93 per verse (Ayah), per pair of translation. And. After pre-processing, the average difference further increased to 2.47. The average difference between the human annotated similarity and the semantic similarity for Surah An-Nasr before preprocessing was found to be 0.93 and after pre-processing, it was reduced to 0.87 per verse (ayah) per pair of translation. The results showed that as expected, the semantic similarity was proven to be better measurement indicator for calculation of the word meaning.