• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1543-1561
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• 2020

Taxonomy of human brain activity is a complicated rather challenging procedure. Due to its multifaceted aspects, including experiment design, stimuli selection and presentation of images other than feature extraction and selection techniques, foster its challenging nature. Although, researchers have focused various methods to create taxonomy of human brain activity, however use of multivariate pattern analysis (MVPA) for image recognition to catalog the human brain activities is scarce. Moreover, experiment design is a complex procedure and selection of image type, color and order is challenging too. Thus, this research bridge the gap by using MVPA to create taxonomy of human brain activity for different categories of images, both colored and gray scale. In this regard, experiment is conducted through EEG testing technique, with feature extraction, selection and classification approaches to collect data from prequalified criteria of 25 graduates of University Technology PETRONAS (UTP). These participants are shown both colored and gray scale images to record accuracy and reaction time. The results showed that colored images produces better end result in terms of accuracy and response time using wavelet transform, t-test and support vector machine. This research resulted that MVPA is a better approach for the analysis of EEG data as more useful information can be extracted from the brain using colored images. This research discusses a detail behavior of human brain based on the color and gray scale images for the specific and unique task. This research contributes to further improve the decoding of human brain with increased accuracy. Besides, such experiment settings can be implemented and contribute to other areas of medical, military, business, lie detection and many others.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.409-416
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• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.205-212
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• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.62-68
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• 2012

Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user's inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.679-685
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• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.182-188
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• 2013

In this paper, we propose a complex-valued support vector machine (SVM) classifier which process the complex valued signal measured by pulse doppler radar (PDR) to identify moving targets from the background. SVM is widely applied in the field of pattern recognition, but features which used to classify are almost real valued data. Proposed complex-valued SVM can classify the moving target using real valued data, imaginary valued data, and cross-information data. To design complex-valued SVM, we consider slack variables of real and complex axis, and use the KKT (Karush-Kuhn-Tucker) conditions for complex data. Also we apply radial basis function (RBF) as a kernel function which use a distance of complex values. To evaluate the performance of the complex-valued SVM, complex valued data from PDR were classified using real-valued SVM and complex-valued SVM. The proposed complex-valued SVM classification was improved compared to real-valued SVM for dog and human, respectively 8%, 10%, have been improved.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.1
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• pp.28-37
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• 2019

In this paper, we propose a real-time system for counting people. The proposed system uses an impulse radio ultra-wideband(IR-UWB) radar to estimate the number of people in a given location. The proposed system uses learning-based classification methods to count people more accurately. In other words, a feature vector database is constructed by exploiting the pattern of reflected signals, which depends on the number of people. Subsequently, a classifier is trained using this database. When a newly received signal data is acquired, the system automatically counts people using the pre-trained classifier. We validated the effectiveness of the proposed algorithm by presenting the results of real-time estimation of the number of people changing from 0 to 10 in an indoor environment.

• Journal of Information Processing Systems
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• v.9 no.2
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• pp.333-348
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• 2013

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately $91.33{\pm}0.67%$ for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.34-42
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• 2014

In this paper an embedding algorithm based on commute time is implemented by organizing patches according to the graph-based metric, and its performance is analyzed by comparing with the results of principal component analysis embedding. It is usual that the dimensionality reduction be done within some acceptable approximation error. However this paper shows the proposed manifold embedding method generates the intrinsic geometry corresponding to the signal despite severe approximation error, so that it can be applied to the areas such as pattern classification or machine learning.