• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2314-2318
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• 2010

For human-robot interaction, a robot should recognize the meaning of human behavior. In the case of static behavior such as face expression and sign language, the information contained in a single image is sufficient to deliver the meaning to the robot. In the case of dynamic behavior such as gestures, however, the information of sequential images is required. This paper proposes behavior classification by using fuzzy classifier to deliver the meaning of dynamic behavior to the robot. The proposed method extracts feature points from input images by a skeleton model, generates a vector space from a differential image of the extracted feature points, and uses this information as the learning data for fuzzy classifier. Finally, we show the effectiveness and the feasibility of the proposed method through experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.735-743
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• 2020

Secure coding is a technique that detects malicious attack or unexpected errors to make software systems resilient against such circumstances. In many cases secure coding relies on static analysis tools to find vulnerable patterns and contaminated data in advance. However, secure coding has the disadvantage of being dependent on rule-sets, and accurate diagnosis is difficult as the complexity of static analysis tools increases. In order to support secure coding, we apply machine learning techniques, such as DNN, CNN and RNN to investigate into finding major weakness patterns shown in secure development coding guides and present machine learning models and experimental results. We believe that machine learning techniques can support detecting security weakness along with static analysis techniques.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.1
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• pp.81-88
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• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.401-408
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• 1997

Human gets almist all of his knoweledge from the recognition and the accumulation of input patterns,image or sound,the he gets theough his eyes and through his ears.Among these means,his chracter recognition,an ability that allows him to recognize characters and understand their meanings through visual information, is now applied to a pattern recognition system using neural network in computer. Recurrent neural network is one of those models that reuse the output value in neural network learning.Recently many studies try to apply this recurrent neural network to the classification of static patterns like off-line handwritten characters. But most of their efforts are not so drrdtive until now.This stusy suggests a new type of recurrent neural network for an deedctive classification of the static patterns such as off-line handwritten chracters.Using the new J-E(Jordan-Elman)neural network model that enlarges and combines Jordan Model and Elman Model,this new type is better than those of before in recobnizing the static patterms such as figures and handwritten-characters.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.

• International Journal of Ocean Engineering and Technology Speciallssue:Selected Papers
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• v.4 no.1
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• pp.51-55
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• 2001

The characteristics of elastic waves emanating from crack initiation in 2024 and 5052 aluminum alloys subject to static and fatigue loading are investigated through laboratory experiments. The objective of the study is to determine difference in the properties of the signals generated from static and fatigue tests and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recoded using non-resonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Three distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the material and the type of fracture associated with the signals. Analysis of the waveforms indicated that some signals could be attributed to plastic deformation associated with static tests. The potential application of the approach in health monitoring of aging aircraft structures using a network of surface mounted broadband sensors is discussed.

• IE interfaces
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• v.16 no.1
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• pp.85-93
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• 2003

The purposes of this study are to investigate perceived discomfort for static joint motions, and to propose rankings for the joint motions based on the perceived discomfort. The perceived discomfort was measured through an experiment using the free modulus method of the magnitude estimation, in which ten healthy college-age female students participated. The results showed that joints, joint motions and their levels significantly affected the perceived discomfort at $\alpha$=0.01, and that the interaction of joints and joint motion levels was also significant. Based on the experimental results, three rankings were proposed by joint and joint motions, by joints and by joint motions, which were very different from the existing ones. Especially, the proposed rankings were different from the males' published before in their order and magnitude. These rankings can be used as a valuable tool for better understanding potentially adverse effects of poor working postures in industrial sites, and as basic data for developing the postural classification scheme.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.393-400
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• 2007

We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Phonetics and Speech Sciences
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• v.9 no.4
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• pp.27-34
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• 2017

The purpose of this work is to examine the English and Korean voiceless plosives from the Buckeye[15] and Seoul[16] corpus in terms of their static spectral characteristics. The plosives were automatically extracted by a Praat script. In order to estimate the percent correctness in the classification of the plosives, discriminant analyses were performed whose trainings were based on four spectral moments, i.e. the center of gravity, variance, skewness and kurtosis as suggested in [6]. Another set of discriminant analyses were performed based on the spectral tilts. In the last set of analyeses, the spectral moments and tilts were both used in the training. Results showed that the correct classification rate did not exceed around 65% in the best case, which suggested that phonetic cues other than the release burst would be necessary including the dynamic spectral aspects and vowel-onset cues.