• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.1
• /
• pp.287-295
• /
• 2017

Recently, MTC (Machine Type Communication) is known as an important part to support IoT (Internet of Things) applications. MTC provides network connectivities between MTC devices without human intervention. In MTC, a large number of devices try to access over communication resource with a short period of time. Due to the limited communication resource, resource contention becomes severe and it brings about access failures of devices. To solve the problem, it needs to regulate device accesses. In this paper, we present an efficient access management scheme. We measure the number of devices which try to access in a certain time period and predict the change of the number of devices in the next time period. Using the predicted change, we control the number of devices which try to access. To verify our scheme, we conduct experiments in terms of success probability, failure probability, collision probability and access delay.

• Journal of the Semiconductor & Display Technology
• /
• v.18 no.1
• /
• pp.97-101
• /
• 2019

Understanding and classification of the human's emotion play an important tasks in interacting with human and machine communication systems. This paper proposes a novel emotion recognition method by extracting facial keypoints, which is able to understand and classify the human emotion, using active Appearance Model and the proposed classification model of the facial features. The existing appearance model scheme takes an expression of variations, which is calculated by the proposed classification model according to the change of human facial expression. The proposed method classifies four basic emotions (normal, happy, sad and angry). To evaluate the performance of the proposed method, we assess the ratio of success with common datasets, and we achieve the best 93% accuracy, average 82.2% in facial emotion recognition. The results show that the proposed method effectively performed well over the emotion recognition, compared to the existing schemes.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2009.05a
• /
• pp.259-262
• /
• 2009

IEC 61850 based substation automation system mainly consists of human machine interface and a various of IEDs in charge of protective and control functions of the substation. Both of them should require the performance verification of IEC 61850 communication services because their communication relation is the same as client and server throughout the digital network. This paper shows a research result on the testing method of basic communication interface of the IEC 61850 based client, which was implemented by the analysis of real communication between HMI and IED of IEC 61850 based substation automation system and refereed to IED IEC 61850 conformance test procedures.

• Geomechanics and Engineering
• /
• v.30 no.1
• /
• pp.75-91
• /
• 2022

This paper aims to show how to use several Machine Learning (ML) methods to estimate the TBM penetration rate systematically (TBM-PR). To this end, 1125 datasets including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), punch slope index (PSI), distance between the planes of weakness (DPW), orientation of discontinuities (alpha angle-α), rock fracture class (RFC), and actual/measured TBM-PRs were established. To evaluate the ML methods' ability to perform, the 5-fold cross-validation was taken into consideration. Eventually, comparing the ML outcomes and the TBM monitoring data indicated that the ML methods have a very good potential ability in the prediction of TBM-PR. However, the long short-term memory model with a correlation coefficient of 0.9932 and a route mean square error of 2.68E-6 outperformed the remaining six ML algorithms. The backward selection method showed that PSI and RFC were more and less significant parameters on the TBM-PR compared to the others.

• Journal of the Ergonomics Society of Korea
• /
• v.31 no.4
• /
• pp.593-599
• /
• 2012

Objective: This study aims to implement a non-contact gesture-based interface for presentation purposes and to analyze the effect of the proposed interface as information transfer assisted device. Background: Recently, research on control device using gesture recognition or speech recognition is being conducted with rapid technological growth in UI/UX area and appearance of smart service products which requires a new human-machine interface. However, few quantitative researches on practical effects of the new interface type have been done relatively, while activities on system implementation are very popular. Method: The system presented in this study is implemented with KINECT$^{(R)}$ sensor offered by Microsoft Corporation. To investigate whether the proposed system is effective as a presentation support tool or not, we conduct experiments by giving several lectures to 40 participants in both a traditional lecture room(keyboard-based presentation control) and a non-contact gesture-based lecture room(KINECT-based presentation control), evaluating their interests and immersion based on contents of the lecture and lecturing methods, and analyzing their understanding about contents of the lecture. Result: We check that whether the gesture-based presentation system can play effective role as presentation supporting tools or not depending on the level of difficulty of contents using ANOVA. Conclusion: We check that a non-contact gesture-based interface is a meaningful tool as a sportive device when delivering easy and simple information. However, the effect can vary with the contents and the level of difficulty of information provided. Application: The results presented in this paper might help to design a new human-machine(computer) interface for communication support tools.

• Journal of Mechanical Science and Technology
• /
• v.19 no.2
• /
• pp.578-588
• /
• 2005

Face robots capable of expressing their emotional status, can be adopted as an efficient tool for friendly communication between the human and the machine. In this paper, we present a face robot actuated with artificial muscle based on dielectric elastomer. By exploiting the properties of dielectric elastomer, it is possible to actuate the covering skin, eyes as well as provide human-like expressivity without employing complicated mechanisms. The robot is driven by seven actuator modules such eye, eyebrow, eyelid, brow, cheek, jaw and neck module corresponding to movements of facial muscles. Although they are only part of the whole set of facial motions, our approach is sufficient to generate six fundamental facial expressions such as surprise, fear, angry, disgust, sadness, and happiness. In the robot, each module communicates with the others via CAN communication protocol and according to the desired emotional expressions, the facial motions are generated by combining the motions of each actuator module. A prototype of the robot has been developed and several experiments have been conducted to validate its feasibility.

• Annual Conference on Human and Language Technology
• /
• 2019.10a
• /
• pp.415-418
• /
• 2019

기계독해 모델에 새로운 도메인을 적용하기 위해서는 도메인에 맞는 데이터가 필요하다. 그러나 추가 데이터 구축은 많은 비용이 발생한다. 사람이 직접 구축한 데이터 없이 적용하기 위해서는 자동 추가 데이터 확보, 도메인 적응의 문제를 해결해야한다. 추가 데이터 확보의 경우 번역, 질의 생성의 방법으로 연구가 진행되었다. 그러나 도메인 적응을 위해서는 새로운 정답 유형에 대한 질의가 필요하며 이를 위해서는 정답 후보 추출, 추출된 정답 후보로 질의를 생성해야한다. 본 논문에서는 이러한 문제를 해결하기 위해 듀얼 포인터 네트워크 기반 정답 후보 추출 모델로 정답 후보를 추출하고, 포인터 제너레이터 기반 질의 생성 모델로 새로운 데이터를 생성하는 방법을 제안한다. 실험 결과 추가 데이터 확보의 경우 KorQuAD, 경제, 금융 도메인의 데이터에서 모두 성능 향상을 보였으며, 도메인 적응 실험에서도 새로운 도메인의 문맥만을 이용해 데이터를 생성했을 때 기존 도메인과 다른 도메인에서 모두 기계독해 성능 향상을 보였다.

• Journal of Multimedia Information System
• /
• v.4 no.1
• /
• pp.39-42
• /
• 2017

Communication between human and machine is having been researched from last few decades and still it's a challenging task because human behavior is unpredictable. When it comes on handwritten digits almost each human has their own writing style. Handwritten digit recognition plays an important role, especially in the courtesy amounts on bank checks, postal code on mail address etc. In our study, we proposed an efficient feature extraction system for recognizing single digit number drawn by mouse or by a finger on a screen. Our proposed method combines basic image processing and reading the strokes of a line drawn. It is very simple and easy to implement in various platform as compare to the system which required high system configuration. This system has been designed, implemented, and tested successfully.

• Proceedings of the KSME Conference
• /
• 2003.04a
• /
• pp.935-939
• /
• 2003

The emotion recognition is one of the most significant interface technologies which make the high level of human-machine communication possible. The central nervous system stimulated by emotional stimuli affects the autonomous nervous system like a heart, blood vessel, endocrine organs, and so on. Therefore bio-signals like HRV, ECG and EEG can reflect one' emotional state. This study investigates the correlation between emotional states and bio-signals to realize the emotion recognition. This study also covers classification of human emotional states, selection of the effective bio-signal and signal processing. The experimental results presented in this paper show possibility of the emotion recognition.

• Journal of information and communication convergence engineering
• /
• v.21 no.2
• /
• pp.110-116
• /
• 2023

Recently, as various examples of machine learning have been applied in the healthcare field, deep learning technology has been applied to various tasks, such as electrocardiogram examination and body composition analysis using wearable devices such as smart watches. To utilize deep learning, securing data is the most important procedure, where human intervention, such as data classification, is required. In this study, we propose a model that uses a clustering algorithm, namely, the K-means clustering, to label body fat according to gender and age considering body size aspects, such as chest circumference and waist circumference, and classifies body fat into five groups from high risk to low risk using a convolutional neural network (CNN). As a result of model validation, accuracy, precision, and recall results of more than 95% were obtained. Thus, rational decision making can be made in the field of healthcare or obesity analysis using the proposed method.