• Korean Journal of Applied Biomechanics
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• v.29 no.4
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• pp.247-254
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• 2019

Objective: The goal of this study was to determine the center of pressure (CoP) complexity pattern in approximate entropy technique between genders at different conditions of running speed. Background: It is conducted to evaluate the complexity pattern of CoP in the increment of running speed to have insights to injury prediction, stability, and auxiliary aids for the foot. Method: Twenty men (age=22.3±1.5 yrs.; height=176.4±5.4 cm; body weight=73.9±8.2 kg) and Twenty women (age=20.8±1.2 yrs.; height=162.8±5.2 cm; body weight=55.0±6.3 kg) with heel strike pattern were recruited for the study. While they were running at 2.22, 3.33, 4.44 m/s speed on a treadmill (instrumented dual belt treadmills, USA) with a force plate, CoP data were collected for the 10 strides. The complexity pattern of the CoP was analyzed using the ApEn technique. Results: The ApEn of the medial-lateral and antero-posterior CoP in the increment of running speed showed significantly difference within genders (p<.05), but there were not statistically significant between genders at all conditions of running speed. Conclusion: Based on the results of this study, CoP complexity pattern in the increment of running speed was limited to be characterized between genders as an indicator to judge the potential injury and stability. Application: In future studies, it is needed to investigate the cause of change for complexity of CoP at various running speed related to this study.

• Architectural research
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• v.13 no.4
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• pp.19-30
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• 2011

This investigation, based on empirical research, examined the role of complexity and order in the aesthetic experience of architectural forms. The basic assumption of this study was that perception in architectural form is a process of interpreting a pattern in a reductive way. Thus, perceptual arousal is not determined by the absolute complexity of a configuration. Rather, the actual perceived complexity is a function of the organization of the system (order). In addition, complexity and order were defined and categorized into four variables according to their significant characteristics; simple order, complex order, random complexity, and lawful complexity. The series of experiments confirmed that there is a point on the psychological complexity dimension which is optimal. By demonstrating that consensual and individual aesthetic preference can be measured to have a unimodal function of relationship with complexity, the results of the experiments indicated that complexity and orderliness are effective design factors for enhancing aesthetics of a building facade. This investigation offered a conceptual framework that relates the physical (architectural form) and psychological factors (complexity and order) operating in the aesthetic experience of building facades.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.1
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• pp.7-14
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• 2021

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.403-404
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• 2023

패턴의 복잡도와 정보량을 계산하는 것은 음성 및 영상 인식을 위하여 향후 더 중요한 정보를 제공하는 단계로 발전할 것으로 기대된다. 패턴의 복잡도를 표현하는 정보 엔트로피의 개념은 정보량 측정외에 데이터의 압축 복원 과정, 데이터의 복잡도 등 다양한 목적으로 활용되고 있다. 본 논문에서는 영상 패턴의 복잡도를 영상 화질의 차이를 분석함으로써 영상 인식 시 지표 가능성을 파악하고자 한다.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.329-336
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• 2017

In video compression, Motion Estimation(ME) limits the performance of image quality and generated bit rates. However, it requires much complexity in the encoder part. Multi-view video uses many cameras at different positions. Multi-view video coding needs huge computational complexity in proportion to the number of the cameras. To reduce computational complexity and maintain the image quality, an effective motion estimation method is proposed in this paper. The proposed method exploiting the characteristics of motion vector distribution and the motion of video. The proposed is a kind of a hierarchical search strategy. This strategy consists of multi-grid rhombus pattern, diagonal pattern, rectangle pattern, and refinement pattern. Experiment results show that the complexity reduction of the proposed method over TZ search method and PBS (Pel Block Search) on JMVC (Joint Multiview Video Coding) can be up to 40~75% and 98% respectively while maintaining similar video image quality and generated bit rates.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.6
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• pp.354-367
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• 2010

There are many methods for analyzing patterns in time series data. Although stock data represents a time series, there are few studies on stock pattern analysis and prediction. Since people believe that stock price changes randomly we cannot predict stock prices using a scientific method. In this paper, we measured the degree of the randomness of stock prices using Kolmogorov complexity, and we showed that there is a strong correlation between the degree and the accuracy of stock price prediction using our semi-global alignment method. We transformed the stock price data to quantized string sequences. Then we measured randomness of stock prices using Kolmogorov complexity of the string sequences. We use KOSPI 690 stock data during 28 years for our experiments and to evaluate our methodology. When a high Kolmogorov complexity, the stock price cannot be predicted, when a low complexity, the stock price can be predicted, but the prediction ratio of stock price changes of interest to investors, is 12% prediction ratio for short-term predictions and a 54% prediction ratio for long-term predictions.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.1
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• pp.11-18
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• 1999

The objective of this study are to figure out the physical attributes of the three predictors in the Informational Approach: complexity, coherence, mystery and to investigate the relationships between these attributes and the preference exploratively. Visual inspection of the scenes relative to their rated levels of the predictors revealed the existence of relationship between these variables and the physical attributes. The following are the summary of the relationship between three predictors and the physical attributes: (1) The level of complexity was associated with the pattern of physical attributes which were the amount of facility, line, shape, color plant and arrangement of the visual elements. (2) The level of coherence was related with the regular arrangement of the visual elements. For example, there was certain pattern founded the color, shape, texture was applied to the various space repetitively and symmetrically. (3) The level of mystery had the relationship with the physical attributes of screen, spatial definition, distance of view, physical accessibility, radiant forest, the depth of space.

• Journal of the Korean Society of Costume
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• v.59 no.2
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• pp.29-41
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• 2009

Perception between real object and recognized subject of human on objective subject is not the same. The reason Is that individual perception of visual design components are transmitted as the image of whole. It is required process of visual perception. Therefore, I developed the vision of seeing image of pattern which is based on Gestalt visual perception theory in clothes. The summary of this study's results is like followings. Extremely antagonistic terms which are specialized by formative characteristics of formative components are clearness and blur of outline/ fixed shape and non-fixed shape/ visuality and tangibility of representation/ simplicity and complexity of structure/ invariability and variability of mobility/ symmetry and asymmetry of arrangements singularity and plurality of group number. The expression of motive shows that clearness, fixed shape, visuality and simplicity pursuit Determination image, and blur, non-fixed shape, tangibility and complexity pursuit Ambiguity image. The arrangements of motive shows that invariability, symmetry and singularity pursuit Order image, and variability asymmetry and plurality pursuit Disorder image. Therefore, the standard of the coordinator of Pattern image positioning is established as Determination and Ambiguity of motive are X-axis, and Order and Disorder of pattern are Y-axis. As the frame of Pattern image positioning, four separated dimensions have made.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.7
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• pp.705-710
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• 2011

In this paper, we proposed the low computational complexity soft-decision e-BCH decoding algorithm based on the Chase algorithm. In order to make the test patterns, it is necessary to re-order the least reliable received symbols. In the process of ordering and finding optimal decoding symbols, high computational complexity is required. Therefore, this paper proposes the method of low computational complexity algorithm for soft-decision e-BCH decoding process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.73-82
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• 2013

In pattern classification, feature selection is an important factor in the performance of classifiers. In particular, when classifying a large number of features or variables, the accuracy and computational time of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. The proposed method consists of two parts: a wrapper part with an improved genetic algorithm(GA) using a new reproduction method and a filter part using mutual information. We also considered feature selection methods based on mutual information(MI) to improve computational complexity. Experimental results show that this method can achieve better performance in pattern recognition problems than other conventional solutions.