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K.B. Sim
School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea

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Journal article
Published: 13 May 2021 in Electronics
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The brain–computer interface (BCI) is a promising technology where a user controls a robot or computer by thinking with no movement. There are several underlying principles to implement BCI, such as sensorimotor rhythms, P300, steady-state visually evoked potentials, and directional tuning. Generally, different principles are applied to BCI depending on the application, because strengths and weaknesses vary according to each BCI method. Therefore, BCI should be able to predict a user state to apply suitable principles to the system. This study measured electroencephalography signals in four states (resting, speech imagery, leg-motor imagery, and hand-motor imagery) from 10 healthy subjects. Mutual information from 64 channels was calculated as brain connectivity. We used a convolutional neural network to predict a user state, where brain connectivity was the network input. We applied five-fold cross-validation to evaluate the proposed method. Mean accuracy for user state classification was 88.25 ± 2.34%. This implies that the system can change the BCI principle using brain connectivity. Thus, a BCI user can control various applications according to their intentions.

ACS Style

Seung-Min Park; Hong-Gi Yeom; Kwee-Bo Sim. User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network. Electronics 2021, 10, 1158 .

AMA Style

Seung-Min Park, Hong-Gi Yeom, Kwee-Bo Sim. User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network. Electronics. 2021; 10 (10):1158.

Chicago/Turabian Style

Seung-Min Park; Hong-Gi Yeom; Kwee-Bo Sim. 2021. "User State Classification Based on Functional Brain Connectivity Using a Convolutional Neural Network." Electronics 10, no. 10: 1158.

Journal article
Published: 10 May 2021 in Electronics
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Recently, to enhance the security of the Internet of Things (IoT), research on blockchain-based encryption algorithms has been actively conducted. However, because blockchains have complex structures and process large amounts of data, there are still many difficulties in using the conventional blockchain-based encryption algorithms in an IoT system that must have low power consumption and be ultra-lightweight. In this study, to address these problems (1) we simplified the conventional Directed Acyclic Graph (DAG)-based blockchain structure, and (2) we proposed a new Advanced Encryption Standard (AES)-Cipher Block Chaining (CBC) algorithm with enhanced security by periodically changing the secret key and initialization vector (IV) in the conventional AES-CBC encryption algorithm. Because the DAG, which is the conventional blockchain structure, randomly transmits data to multiple blocks, there may be overlapping blocks, and the quantity of transmitted data is not limited; thus, the time and power consumption for encryption and decryption increase. In this study, a simplified DAG was designed to address these problems so that packets can be transmitted only to three blocks, without overlapping. Finally, to verify the effectiveness of the algorithm proposed in this paper, an IoT system consisting of 10 clients and one server was implemented in hardware, and an experiment was conducted. Through the experiment, it was confirmed that when the proposed AES-CBC algorithm was used, the time taken and the amount of power consumed for encryption and decryption were reduced by about 20% compared to the conventional AES-CBC algorithm.

ACS Style

Sung-Won Lee; Kwee-Bo Sim. Design and Hardware Implementation of a Simplified DAG-Based Blockchain and New AES-CBC Algorithm for IoT Security. Electronics 2021, 10, 1127 .

AMA Style

Sung-Won Lee, Kwee-Bo Sim. Design and Hardware Implementation of a Simplified DAG-Based Blockchain and New AES-CBC Algorithm for IoT Security. Electronics. 2021; 10 (9):1127.

Chicago/Turabian Style

Sung-Won Lee; Kwee-Bo Sim. 2021. "Design and Hardware Implementation of a Simplified DAG-Based Blockchain and New AES-CBC Algorithm for IoT Security." Electronics 10, no. 9: 1127.

Journal article
Published: 09 April 2020 in Applied Sciences
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In this paper, we propose an advanced parameter-setting-free (PSF) scheme to solve the problem of setting the parameters for the harmony search (HS) algorithm. The use of the advanced PSF method solves the problems of the conventional PSF scheme that results from a large number of iterations and shows good results compared to fixing the parameters required for the HS algorithm. In addition, unlike the conventional PSF method, the advanced PSF method does not use additional memory. We expect the advanced PSF method to be applicable to various fields that use the HS algorithm because it reduces the memory utilization for operations while obtaining better results than conventional PSF schemes.

ACS Style

Yong-Woon Jeong; Seung-Min Park; Zong Woo Geem; Kwee-Bo Sim. Advanced Parameter-Setting-Free Harmony Search Algorithm. Applied Sciences 2020, 10, 2586 .

AMA Style

Yong-Woon Jeong, Seung-Min Park, Zong Woo Geem, Kwee-Bo Sim. Advanced Parameter-Setting-Free Harmony Search Algorithm. Applied Sciences. 2020; 10 (7):2586.

Chicago/Turabian Style

Yong-Woon Jeong; Seung-Min Park; Zong Woo Geem; Kwee-Bo Sim. 2020. "Advanced Parameter-Setting-Free Harmony Search Algorithm." Applied Sciences 10, no. 7: 2586.

Journal article
Published: 31 March 2019 in The International Journal of Fuzzy Logic and Intelligent Systems
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ACS Style

Sung-Won Lee; Je-Hun Yu; Seung Min Park; Kwee-Bo Sim. Visual Speech Recognition of Korean Words Using Convolutional Neural Network. The International Journal of Fuzzy Logic and Intelligent Systems 2019, 19, 1 -9.

AMA Style

Sung-Won Lee, Je-Hun Yu, Seung Min Park, Kwee-Bo Sim. Visual Speech Recognition of Korean Words Using Convolutional Neural Network. The International Journal of Fuzzy Logic and Intelligent Systems. 2019; 19 (1):1-9.

Chicago/Turabian Style

Sung-Won Lee; Je-Hun Yu; Seung Min Park; Kwee-Bo Sim. 2019. "Visual Speech Recognition of Korean Words Using Convolutional Neural Network." The International Journal of Fuzzy Logic and Intelligent Systems 19, no. 1: 1-9.

Journal article
Published: 16 August 2018 in Cognitive Systems Research
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Facial expressions convey not only emotions but also communicative information. Therefore, facial expressions should be analysed to understand communication. The objective of this study is to develop an automatic facial expression analysis system for extracting ngonverbal communicative information. This study focuses on specific communicative information: emotions expressed through facial movements and the direction of the expressions. We propose a multi-tasking deep convolutional network (DCN) to classify facial expressions, detect the facial regions, and estimate face angles. We reformulate facial region detection and face angle estimation as regression problems and add task-specific output layers in the DCN’s architecture. Experimental results show that the proposed method performs all tasks accurately. In this study, we show the feasibility of the multi-tasking DCN for extracting nonverbal communicative information from a human face.

ACS Style

Heereen Shim; Kyung-Hwan Cho; Kwang-Eun Ko; In-Hoon Jang; Kwee-Bo Sim. Multi-tasking deep convolutional network architecture design for extracting nonverbal communicative information from a face. Cognitive Systems Research 2018, 52, 658 -667.

AMA Style

Heereen Shim, Kyung-Hwan Cho, Kwang-Eun Ko, In-Hoon Jang, Kwee-Bo Sim. Multi-tasking deep convolutional network architecture design for extracting nonverbal communicative information from a face. Cognitive Systems Research. 2018; 52 ():658-667.

Chicago/Turabian Style

Heereen Shim; Kyung-Hwan Cho; Kwang-Eun Ko; In-Hoon Jang; Kwee-Bo Sim. 2018. "Multi-tasking deep convolutional network architecture design for extracting nonverbal communicative information from a face." Cognitive Systems Research 52, no. : 658-667.

Journal article
Published: 17 July 2018 in Optik
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Hyperparameters determine layer architecture in the feature extraction step of a convolutional neural network (CNN), and this affects classification accuracy and learning time. In this paper, we propose a method to improve CNN performance by hyperparameter tuning in the feature extraction step of CNN. In the proposed method, the hyperparameter is adjusted using a parameter-setting-free harmony search (PSF-HS) algorithm, which is a metaheuristic optimization method. In the PSF-HS algorithm, the hyperparameter to be adjusted is set as the harmony, and harmony memory is generated after generating the harmony. Harmony memory is updated based on the loss of a CNN. A simulation using CNN architecture with reference to LeNet-5 and a MNIST dataset, and a simulation using the CNN architecture with reference to CifarNet and a Cifar-10 dataset are performed. By two simulations, it is possible to improve the performance by tuning the hyperparameters in CNN architectures proposed in the past.

ACS Style

Woo-Young Lee; Seung-Min Park; Kwee-Bo Sim. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik 2018, 172, 359 -367.

AMA Style

Woo-Young Lee, Seung-Min Park, Kwee-Bo Sim. Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm. Optik. 2018; 172 ():359-367.

Chicago/Turabian Style

Woo-Young Lee; Seung-Min Park; Kwee-Bo Sim. 2018. "Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm." Optik 172, no. : 359-367.

Image and vision processing and display technology
Published: 01 September 2017 in Electronics Letters
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A real-time scheme for detecting object entities in real-time among a set of objects contained in the same class category is proposed. Building a unified framework for real-time object entity detection system without an additional training process to distinguish the object entities while minimising the loss of accuracy is focused. The experimental results on a benchmark dataset demonstrate that the method shows outstanding precision performance while achieving state-of-the-art object detection speed.

ACS Style

K.E. Ko; K.B. Sim. Real‐time object entity detection system for smart surveillance application. Electronics Letters 2017, 53, 1304 -1306.

AMA Style

K.E. Ko, K.B. Sim. Real‐time object entity detection system for smart surveillance application. Electronics Letters. 2017; 53 (19):1304-1306.

Chicago/Turabian Style

K.E. Ko; K.B. Sim. 2017. "Real‐time object entity detection system for smart surveillance application." Electronics Letters 53, no. 19: 1304-1306.

Article
Published: 25 October 2016 in International Journal of Control, Automation and Systems
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For the brain-computer interface system (BCI), pre-processing has an important role to ensure system performance. However, the speech recognition system using electroencephalogram (EEG) is weak against temporal effects. Therefore, in general cases, wavelet transform has been used to cope with the temporal effects and non-stationary characteristic of EEG. The discrete version of wavelet transform, called DWT, requires a filter of the system for use in downsampling the signal. In other words, it is important to determine the suitable type of filter. In many cases, it is difficult to find an adequate filter for DWT because of differences in the characteristics of the input signal. In this paper, we proposed a heuristic approach to finding the optimal filter of the system for EEG signals. The harmony search algorithm (HSA) was used for finding of the optimal filter. In the learning process with the EEG system, the optimal wavelet filter could be found, which is automatically designed for subject personality.

ACS Style

Seung-Min Park; Tae-Ju Lee; Kwee-Bo Sim. Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform. International Journal of Control, Automation and Systems 2016, 14, 1582 -1587.

AMA Style

Seung-Min Park, Tae-Ju Lee, Kwee-Bo Sim. Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform. International Journal of Control, Automation and Systems. 2016; 14 (6):1582-1587.

Chicago/Turabian Style

Seung-Min Park; Tae-Ju Lee; Kwee-Bo Sim. 2016. "Heuristic feature extraction method for BCI with harmony search and discrete wavelet transform." International Journal of Control, Automation and Systems 14, no. 6: 1582-1587.

Information and communications
Published: 01 October 2016 in Electronics Letters
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A method for autonomous segmentation of motion primitives from continuous observation of goal-directed behaviours is proposed. In the proposed method, the iterative patterns of the observed behaviours, which are viewed as motion primitives, are segmented using a stochastic topology preserving map (TPM). The stochastic TPM, which is derived from a combination of self-organising map and Gaussian mixture, provides substantial capabilities for unsupervised learning and clustering of high-dimensional data sequences. The results of experiments conducted, in which the proposed method was applied to a dataset of daily activities captured using a Kinect, verify that the proposed method is reliable.

ACS Style

K.E. Ko; K.B. Sim. Unsupervised stochastic segmentation of behaviour for learning by demonstration. Electronics Letters 2016, 52, 1767 -1769.

AMA Style

K.E. Ko, K.B. Sim. Unsupervised stochastic segmentation of behaviour for learning by demonstration. Electronics Letters. 2016; 52 (21):1767-1769.

Chicago/Turabian Style

K.E. Ko; K.B. Sim. 2016. "Unsupervised stochastic segmentation of behaviour for learning by demonstration." Electronics Letters 52, no. 21: 1767-1769.

Journal article
Published: 01 October 2016 in Optik
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ACS Style

Je-Hun Yu; Kwee-Bo Sim. Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik 2016, 127, 9711 -9718.

AMA Style

Je-Hun Yu, Kwee-Bo Sim. Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik. 2016; 127 (20):9711-9718.

Chicago/Turabian Style

Je-Hun Yu; Kwee-Bo Sim. 2016. "Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram." Optik 127, no. 20: 9711-9718.

Journal article
Published: 20 July 2016 in Sensors
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There are several types of intersections such as merge-roads, diverge-roads, plus-shape intersections and two types of T-shape junctions in urban roads. When an autonomous vehicle encounters new intersections, it is crucial to recognize the types of intersections for safe navigation. In this paper, a novel intersection type recognition method is proposed for an autonomous vehicle using a multi-layer laser scanner. The proposed method consists of two steps: (1) static local coordinate occupancy grid map (SLOGM) building and (2) intersection classification. In the first step, the SLOGM is built relative to the local coordinate using the dynamic binary Bayes filter. In the second step, the SLOGM is used as an attribute for the classification. The proposed method is applied to a real-world environment and its validity is demonstrated through experimentation.

ACS Style

Jhonghyun An; Baehoon Choi; Kwee-Bo Sim; Euntai Kim. Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner. Sensors 2016, 16, 1123 .

AMA Style

Jhonghyun An, Baehoon Choi, Kwee-Bo Sim, Euntai Kim. Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner. Sensors. 2016; 16 (7):1123.

Chicago/Turabian Style

Jhonghyun An; Baehoon Choi; Kwee-Bo Sim; Euntai Kim. 2016. "Novel Intersection Type Recognition for Autonomous Vehicles Using a Multi-Layer Laser Scanner." Sensors 16, no. 7: 1123.

Research article
Published: 07 November 2013 in Journal of Applied Mathematics
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This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.

ACS Style

Tae-Ju Lee; Seung-Min Park; Kwee-Bo Sim. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI. Journal of Applied Mathematics 2013, 2013, 1 -9.

AMA Style

Tae-Ju Lee, Seung-Min Park, Kwee-Bo Sim. Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI. Journal of Applied Mathematics. 2013; 2013 (2):1-9.

Chicago/Turabian Style

Tae-Ju Lee; Seung-Min Park; Kwee-Bo Sim. 2013. "Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI." Journal of Applied Mathematics 2013, no. 2: 1-9.

Regular paper
Published: 15 June 2013 in International Journal of Control, Automation and Systems
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This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.

ACS Style

Kwang-Eun Ko; Kwee-Bo Sim. Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks. International Journal of Control, Automation and Systems 2013, 11, 608 -613.

AMA Style

Kwang-Eun Ko, Kwee-Bo Sim. Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks. International Journal of Control, Automation and Systems. 2013; 11 (3):608-613.

Chicago/Turabian Style

Kwang-Eun Ko; Kwee-Bo Sim. 2013. "Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks." International Journal of Control, Automation and Systems 11, no. 3: 608-613.

Conference paper
Published: 01 January 2012 in Computer Vision
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Communication between machine and human is one of important application of brain-computer interface (BCI). There are still obtained many kinds of noise in the recorded human signals, specifically brain signal is electroencelophagram (EEG). It caused by outer and inner of the brain signals such as artifacts in signal, properties of EEG signal nonstationary, variant by time and subjects, which affect to the classification results. The most famous spatial filter in BCI context is common spatial patterns (CSP), maximize one condition while minimize the other condition using covariance. So in this experiment we recorded signal by using auditory stimuli to reduce artifact by gaze attention. Extended CSP methods were applied in this experiment to upgrade the classification accuracy of brain source separate by independent component analysis (ICA). We supposed this combination could purify the signals as 2 steps and increased the accuracy classification.

ACS Style

Thanh Ha Nguyen; Seung-Min Park; Kwang-Eun Ko; Kwee-Bo Sim. Improvement of Spatial Filtering by Using ICA in Auditory Stimuli BCI Systems of Hand Movement. Computer Vision 2012, 7425, 477 -484.

AMA Style

Thanh Ha Nguyen, Seung-Min Park, Kwang-Eun Ko, Kwee-Bo Sim. Improvement of Spatial Filtering by Using ICA in Auditory Stimuli BCI Systems of Hand Movement. Computer Vision. 2012; 7425 ():477-484.

Chicago/Turabian Style

Thanh Ha Nguyen; Seung-Min Park; Kwang-Eun Ko; Kwee-Bo Sim. 2012. "Improvement of Spatial Filtering by Using ICA in Auditory Stimuli BCI Systems of Hand Movement." Computer Vision 7425, no. : 477-484.

Conference paper
Published: 01 January 2012 in Computer Vision
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Subject dependent nature of electroencelography (EEG) signal elicits in the imagination task cause the drop of accuracy of the classifier in the brain-computer interface when the system apply in different subjects or crossing session experiment. The main components that have effect most in this problem are spatial-spectral-temporal parameter of the EEG signal that need to extract to find the optimal solution in the BCI system. In this paper we proposed a method for extracting the optimal parameters based on particle swarm optimization algorithm. First EEG signals were enhanced by Laplace and band pass filter. Optimal spatio-spectral-temporal component of Principle Component Analysis were search by Particle Swarm Optimization (PSO) using Short Time Fourier Transform features and classification error rate from Support Vector Machine (SVM) as fitness function. With optimal parameters, principle component from the STFT features were extracted and combined into single optimal feature vector. 5 fold-cross validations are applied to SVM.

ACS Style

Pharino Chum; Seung-Min Park; Kwang-Eun Ko; Kwee-Bo Sim. Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface. Computer Vision 2012, 7425, 469 -476.

AMA Style

Pharino Chum, Seung-Min Park, Kwang-Eun Ko, Kwee-Bo Sim. Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface. Computer Vision. 2012; 7425 ():469-476.

Chicago/Turabian Style

Pharino Chum; Seung-Min Park; Kwang-Eun Ko; Kwee-Bo Sim. 2012. "Particle Swarm Optimization Based Optimal Spatial-Spectral-Temporal Component Search in Motor Imagery Brain-Computer Interface." Computer Vision 7425, no. : 469-476.

Journal article
Published: 12 October 2011 in International Journal of Control, Automation and Systems
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Many real-world problems involve simultaneous optimization of several incommensurable and often competing objectives. In the search for solutions to multi-objective optimization problems (MOPs), we find that there is no single optimum but rather a set of optimums known as the “Pareto optimal set”. Co-evolutionary algorithms are well suited to optimization problems which involve several often competing objectives. Co-evolutionary algorithms are aimed at evolving individuals through individuals competing in an objective space. In order to approximate the ideal Pareto optimal set, the search capability of diverse individuals in an objective space can be used to determine the performance of evolutionary algorithms. Non-dominated memory and Euclidean distance selection mechanisms for co-evolutionary algorithms have the goal of overcoming the limited search capability of diverse individuals in the population space. In this paper, we propose a method for maintaining population diversity in game model-based co-evolutionary algorithms, and we evaluate the effectiveness of our approach by comparing it with other methods through rigorous experiments on several MOPs.

ACS Style

Seung-Min Park; Kwang-Eun Ko; Junheong Park; Kwee-Bo Sim. Game model-based co-evolutionary algorithm with non-dominated memory and Euclidean distance selection mechanisms for multi-objective optimization. International Journal of Control, Automation and Systems 2011, 9, 924 -932.

AMA Style

Seung-Min Park, Kwang-Eun Ko, Junheong Park, Kwee-Bo Sim. Game model-based co-evolutionary algorithm with non-dominated memory and Euclidean distance selection mechanisms for multi-objective optimization. International Journal of Control, Automation and Systems. 2011; 9 (5):924-932.

Chicago/Turabian Style

Seung-Min Park; Kwang-Eun Ko; Junheong Park; Kwee-Bo Sim. 2011. "Game model-based co-evolutionary algorithm with non-dominated memory and Euclidean distance selection mechanisms for multi-objective optimization." International Journal of Control, Automation and Systems 9, no. 5: 924-932.

Journal article
Published: 04 June 2011 in International Journal of Control, Automation and Systems
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To improve the performance of classification algorithms, we proposed a new varianceconsidered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.

ACS Style

Hong-Gi Yeom; Seung-Min Park; Junheong Park; Kwee-Bo Sim. Superiority demonstration of variance-considered machines by comparing error rate with support vector machines. International Journal of Control, Automation and Systems 2011, 9, 595 -600.

AMA Style

Hong-Gi Yeom, Seung-Min Park, Junheong Park, Kwee-Bo Sim. Superiority demonstration of variance-considered machines by comparing error rate with support vector machines. International Journal of Control, Automation and Systems. 2011; 9 (3):595-600.

Chicago/Turabian Style

Hong-Gi Yeom; Seung-Min Park; Junheong Park; Kwee-Bo Sim. 2011. "Superiority demonstration of variance-considered machines by comparing error rate with support vector machines." International Journal of Control, Automation and Systems 9, no. 3: 595-600.

Conference paper
Published: 01 January 2010 in Computer Vision
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If we want to recognize the human’s emotion via the face-to-face interaction, first of all, we need to extract the emotional features from the facial image and recognize the emotional states. . Our facial emotional feature detection and extracting based on Active Appearance Models (AAM) with Ekman’s Facial Action Coding System (FACS). Our approach to facial emotion recognition lies in the dynamic and probabilistic framework based on Dynamic Bayesian Network (DBN). The active appearance model (AAM) is a well-known method that can represent a non-rigid object, such as face, facial expression. In this paper, our approach to facial feature extraction lies in the proposed feature extraction method based on combining AAM with Facial Action Units of Facial Action Coding System (FACS) for automatically modeling and extracting the facial emotional features. Also, we use the Dynamic Bayesian Networks (DBNs) for modeling and understanding the temporal phases of facial expressions in image sequences.

ACS Style

Kwang-Eun Ko; Kwee-Bo Sim. Emotion Recognition in Facial Image Sequences Using a Combination of AAM with FACS and DBN. Computer Vision 2010, 6424, 702 -712.

AMA Style

Kwang-Eun Ko, Kwee-Bo Sim. Emotion Recognition in Facial Image Sequences Using a Combination of AAM with FACS and DBN. Computer Vision. 2010; 6424 ():702-712.

Chicago/Turabian Style

Kwang-Eun Ko; Kwee-Bo Sim. 2010. "Emotion Recognition in Facial Image Sequences Using a Combination of AAM with FACS and DBN." Computer Vision 6424, no. : 702-712.

Journal article
Published: 08 October 2009 in International Journal of Control, Automation and Systems
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Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.

ACS Style

Kwang-Eun Ko; Hyun-Chang Yang; Kwee-Bo Sim. Emotion recognition using EEG signals with relative power values and Bayesian network. International Journal of Control, Automation and Systems 2009, 7, 865 -870.

AMA Style

Kwang-Eun Ko, Hyun-Chang Yang, Kwee-Bo Sim. Emotion recognition using EEG signals with relative power values and Bayesian network. International Journal of Control, Automation and Systems. 2009; 7 (5):865-870.

Chicago/Turabian Style

Kwang-Eun Ko; Hyun-Chang Yang; Kwee-Bo Sim. 2009. "Emotion recognition using EEG signals with relative power values and Bayesian network." International Journal of Control, Automation and Systems 7, no. 5: 865-870.

Journal article
Published: 01 January 2008 in Electronics Letters
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A ring-type pulse oximeter sensor attached to the finger and its 24-hour ambulatory heart rate monitoring system are presented. The PCB has been designed with a light-to-frequency converter and the CPU with a built-in Zigbee stack for simple and low power consumption. Also designed is the algorithm using a least square estimator to calibrate various signal distortions caused by motion artefacts for a proper accuracy.

ACS Style

I.-H. Jang; H.-G. Yeom; K.-B. Sim. Ring sensor and heart rate monitoring system for sensor network applications. Electronics Letters 2008, 44, 1393 -1394.

AMA Style

I.-H. Jang, H.-G. Yeom, K.-B. Sim. Ring sensor and heart rate monitoring system for sensor network applications. Electronics Letters. 2008; 44 (24):1393-1394.

Chicago/Turabian Style

I.-H. Jang; H.-G. Yeom; K.-B. Sim. 2008. "Ring sensor and heart rate monitoring system for sensor network applications." Electronics Letters 44, no. 24: 1393-1394.