• Journal of IKEEE
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• v.13 no.2
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• pp.159-166
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• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.592-599
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• 2020

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

• The Journal of Korean Physical Therapy
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• v.26 no.4
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• pp.269-273
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• 2014

Purpose: The purpose of this study is to examine the effects of using a horseback riding simulation on static balance in children with cerebral palsy. Methods: This study was conducted with 30 children with cerebral palsy at levels I~IV in the Gross Motor Function Classification System (GMFCS), who were randomly divided into a control group and a hippotherapy group. Both the control group and the experimental group received NDT for 30 minutes per session, four times per week, for ten weeks, while the experimental group also received hippotherapy, 15 minutes per session, four times per week, for ten weeks, after the neurodevelopmental treatment (NDT). The horseback riding simulators JOBA (JEU7805, Panasonic, 일본) used in this study simulated actual horse movements; static balance was measured in each group before the exercise and five weeks and ten weeks after the beginning of the exercise using a pedoscan system (Diers Pedo, Germany). Results: The intergroup effects on static balance were tested, and the results showed no significant differences (p<0.05). Conclusion: The horseback riding simulation exercise was shown to be effective for the static balance of children with cerebral palsy. Therefore, additional studies should be conducted with more children with CP divided according to type.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.273-280
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• 2020

Automatic Static Analysis Tools help developers to quickly find potential defects in source code with less effort. However, the tools reports a large number of false positive warnings which do not have to fix. In our study, we proposed an artificial neural network-based warning classification method using topic models of source code blocks. We collect revisions for fixing bugs from software change management (SCM) system and extract code blocks modified by developers. In deep learning stage, topic distribution values of the code blocks and the binary data that present the warning removal in the blocks are used as input and target data in an simple artificial neural network, respectively. In our experimental results, our warning classification model based on neural network shows very high performance to predict label of warnings such as true or false positive.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.11
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• pp.1225-1232
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• 2009

This study suggests a structural design process for the upper control arm installed at a vehicle. Static strength and durability are the most important responses in the structural design of a control arm. This study considers the static strength in the optimization process. The inertia relief method for FE analysis is utilized to simulate the static loading conditions. According to the classification of structural optimization, the structural design of a control arm is included in the category of shape optimization. In this study, the metamodel technique using the kriging method is adopted to obtain the minimum weight satisfying the strength constraint. Then, the final design is suggested by considering the durability criteria. The durability assessment is obtained by the index of fatigue durability called the SWT (Smith-Watson-Topper) index. The final optimum shape has been proposed by trial and error method.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.882-895
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• 2020

The amount of malware increases exponentially every day and poses a threat to networks and operating systems. Most new malware is a variant of existing malware. It is difficult to deal with numerous malware variants since they bypass the existing signature-based malware detection method. Thus, research on automated methods of detecting and processing variant malware has been continuously conducted. This report proposes a method of extracting feature data from files and detecting malware using machine learning. Feature data were extracted from 7,000 malware and 3,000 benign files using static and dynamic malware analysis tools. A malware classification model was constructed using multiple DNN, XGBoost, and RandomForest layers and the performance was analyzed. The proposed method achieved up to 96.3% accuracy.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.122-125
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• 2008

Even though many researchers studied the foot shape and dimension, those applications lacked. The purpose of this study was to develop insole pattern of elderly women according to footprint. Discrepancy in the classification criteria among of foot parameters complicates attempts for elderly women classification of foot sole. To develop a footprint-based classification technique for the classification of foot sole types by allowing simultaneous use of several parameters. Foot sole data from static standing footprints were recorded from 48 elderly women. The factors of footprint shape were determined. Cluster analysis was applied to obtain individual foot sole classifications. The classification model of foot insole is proposed for a classification of footprint in elderly women. An application of ANOVA, Duncan's analysis, frequency analysis, factor analysis, and cluster analysis have been made to footprint data. In order to make clear foot sole characteristics, the factors of footprint shape have been discussed. The results are as follows. The factors of footprint shape have been classified into four types: foot length, sole slope, outside sole slope, and foot width. The types of foot sole shape have been classified into four types: longed, shortened, outside sloped, and toes sloped.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.19 no.3
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• pp.173-184
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• 2015

Objectives The purpose of this study is to understand formation courses of the ten types of LBP (十種腰痛) in Korean medicine through reviewing classic literatures. Methods We summarized sentences describing syndrome differentiation of LBP directly in Uibujeonrok (醫部全錄) and Donguibogam (東醫寶鑑), and then organized similarities and differences among diagnostic factors described in the classic literatures. Results In most of the classics LBP was classified according to the cause but the causes varied depending on the classic literatures. Cheonkeumbang (千金方) tried to suggest a reasonable classification of LBP in a relatively early age. In Dangyesimbeop (丹溪心法) the causes of LBP were divided into 6 factors; qi movement stagnation (氣鬱), dampness-heat (濕熱), kidney deficiency (腎虛), static blood (瘀血), sprain (挫閃) and phlegm accumulation (積痰). It had a lot of influence on the classic literatures published later. Donguibogam was also influenced by the Dangyesimbeop and the ten types of LBP in Donguibogam was similar to the information on the classification shown in Uihakipmun (醫學入門) and Uijongpildok (醫宗必讀). Conclusions We verified universality of the ten types of LBP; kidney deficiency, phlegm-retained fluid (痰飮), food accumulation (食積), sprain, static blood, wind (風), cold (寒), dampness (濕), dampness-heat and qi (氣).