• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.29-40
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• 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 Digital Contents Society
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• v.19 no.4
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• pp.821-827
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• 2018

Affective computing has gained increasing interest in the recent years with the development of potential applications in Human computer interaction (HCI) and healthcare. Although momentous research has been done on human emotion recognition, however, in comparison to speech and facial expression less attention has been paid to physiological signals. In this paper, Electroencephalogram (EEG) signals from different brain regions were investigated using modified wavelet energy features. For minimization of redundancy and maximization of relevancy among features, mRMR algorithm was deployed significantly. EEG recordings of a publically available "DEAP" database have been used to classify four classes of emotions with Multi class Support Vector Machine. The proposed approach shows significant performance compared to existing algorithms.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1408-1415
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• 2007

To operate Platform Screen Door (PSD) automatically, the operational data of PSD and the train must be interfaced properly. PSD and train are operated together by the signaling system in Automatic Train Operation (ATO) signaling system zone where interfaces are provided between PSD and train. However, the additional PSD interface system with train is required in train operating zone without the interface between PSD and train. For the PSD interface, the wireless communication system or the train status (train correct stop status / train door status) detection system has been used. Seoul Metro line No.2. has the PSD wireless communication system using 447MHz band RF. For the safety of PSD operation, it is required to prevent RF interference in the subway environment where many frequencies exist. In this paper, the PSD wireless communication system is developed using 2.4GHz band RF to prevent the interference of the wireless communication and increase the traffic of the PSD interface. Furthermore, the improved system can store, manage and display the PSD and train operational data using Human Machine Interface (HMI) in the train's driver cab.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1229-1234
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• 2004

Face robots capable of expressing their emotional status, can be adopted as an e cient tool for friendly communication between the human and the machine. In this paper, we present a face robot actuated with arti cial muscle based on dielectric elastomer. By exploiting the properties of polymers, it is possible to actuate the covering skin, and provide human-like expressivity without employing complicated mechanisms. The robot is driven by seven types of actuator modules such as 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 su cient to generate six fundamental facial expressions such as surprise, fear, angry, disgust, sadness, and happiness. 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1249-1254
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• 2014

Big data based on numerous data made by the people are used in order to obtain useful information. We can obtain more useful information if it can apply machine learning techniques added deformation of human memory on the characteristics of the computer program. And big data is predicted by using these conclusions. Humans are used to remember similar data as an original data, so big data processing technology should reflect these human characteristics. In this study, this algorithm to provide the selectivity of information is proposed. This algorithm is the technology to reflect the above factors. This algorithm is selected the data with high selectivity to determine similar data based on the deformation characteristics of the data.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.604-607
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• 2018

From the last past decade, the Internet of Thing (IoT) area has attracted a lot of attention from researchers. It is said to be a promising technology with great impact in people life, since it redefines the relationship objects have with Human and between themselves. It allows objects to gather data from the real world and communicate with others through the internet. This enabled many opportunities for service providers, companies, factories, environmental monitoring, healthcare, smart cities, and soon. Therefore, today, IoT is densely used in various domains of life, and knows an exponential growth. However, although many advancements have been achieved, several challenges keep causing issues and still need to be overcome. This paper gives an overview on the current trend issues in IoT on which researchers are focusing. It's also explores different proposed frameworks to allow the application of cognitive computing as an integrated process of an Internet of things (IoT) systems, to bring a great advanced in the way machine may communicate with human and their surroundings. This is known as cognitive IoT (CIoT), which allows machines to produce a human-like behavior, then providing enhanced level of capabilities to IoT.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.48-53
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• 2005

The world, coming into the 21st century, is preparing a new revolution called a knowledge-based society after the industrial society. The interest of the world is concentrated on information technology, nano-technology and biotechnology. In particular, the nano-technology of which study was originally started from an alternative for overcoming semiconductor micro-technology. It can be applied to most industry subject such as electronics, information and communication, machinery, chemistry, bioengineering, energy, etc. They are emerging into the technology that can change civilization of human beings. Specially, ultra precision machining is quickly applied to nano-technology in the field of machinery. Lately, with rapid development of electronics industry and optic industry, there are needs for super precision finishing of various core parts required in such related apparatuses. This paper handles stability of a super precision micro cutting machine that is a core unit of such a super precision finisher, and analyzes the results depending on the hinge type and material change, using FEM analysis. By reviewing the stability, it is possible to achieve the effect of basic data collection for unit control and to reduce trials and errors in unit design and manufacturing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.2
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• pp.456-463
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• 2004

Non-intrusive methods based on active remote IR illumination for eye tracking is important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tacking methodology that works under variable and realistic lighting conditions. Based on combining the bright-Pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils ale not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tacking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.48-51
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• 2013

This paper described an emotional communication service based on context awareness. Especially it provides with a natural as-like a person communication in the interactive object service between machine and person. The machine should behave as naturally like human. For these things the service object has to provide the function which analyzing and storing users' behavior and handling it. This paper contains the way how to analyze and handle users' pattern by using context awareness. Furthermore, we have studied the method which makes users feel like talking with machine by re-writing messages through 'purpose-extraction algorithm' and 'message re-writing algorithm', which allow service become M2P on networks environment. Moreover we developed 'message recommending algorithm' which recommend several messages by analyzing users' past messages. This emotional communication technology can provide more efficient and user-friendly service by providing personified service on network. Furthermore we can expect this messenger system provided from emotional communication service based on context awareness can be applied a plenty of application services.