• Korean Journal of Cognitive Science
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• v.26 no.4
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• pp.377-392
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• 2015

Korean semantic role labeling (SRL) is usually performed by a machine learning and requires a lot of corpus. However, the Korean PropBank used in Korean SRL system is less than PropBank. It leads to a low performance. Therefore, we expand the annotated corpus and verb frames for Korean SRL system to expand the Korean PropBank corpus. Most of the SRL system have a domain-dependent performance so, the performance may decrease if domain was changed. In this paper, we use the domain adaptation technique to reduce decreasing performance with the existing corpus and the small size of new domain corpus. We apply the domain adaptation technique to Structural SVM and Deep Neural Network. The experimental result show the effectiveness of the domain adaptation technique.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.357-365
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• 2022

Refrigerant mischarging is one of the most frequently occurring failure modes in air conditioners, and both undercharging and overcharging degrade cooling performance. Therefore, it is important to accurately determine the amount of charged refrigerant. In this study, a support vector machine (SVM) model was developed to multi-classify the refrigerant mischarge through steady-state identification via fuzzy clustering techniques. For steady-state identification, a fuzzy clustering algorithm was applied to the air conditioner operation data using the difference between moving averages. The identification results using the proposed method were compared with those using existing steady-state determination techniques studied through the inversed Fisher's discriminant ratio (IFDR). Subsequently, the main features were selected using minimum redundancy maximum relevance (mRMR) considering the correlation among candidate features, and an SVM multi-classification model was devised using the derived features. The proposed method achieves satisfactory accuracy and robustness from test data collected in the new domain.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Advances in aircraft and spacecraft science
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• v.7 no.3
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• pp.251-269
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• 2020

Euclid is an optical/near-infrared survey mission of the European Space Agency (ESA) to investigate the nature of dark energy, dark matter and gravity by observing the geometry of the Universe and the formation of structures over cosmological timescales. The Euclid spacecraft mechanical architecture comprises the Payload Module (PLM) and the Service Module (SVM) connected by an interface structure designed to maximize thermal and mechanical decoupling. This paper shortly illustrates the mechanical system of the spacecraft and the mechanical verification philosophy which is based on the Structural and Thermal Model (STM), built at flight standard for structure and thermal qualification and the Proto Flight Model (PFM), used to complete the qualification programme. It will be submitted to a proto-flight test approach and it will be suitable for launch and flight operations. Within the overall verification approach crucial mechanical tests have been successfully performed (2018) on the SVM platform and on the sunshield (SSH) subsystem: the SVM platform static test, the SSH structure modal survey test and the SSH sine vibration qualification test. The paper reports the objectives and the main results of these tests.

• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.19-28
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• 2023

This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-φ) curve and the effect of particular characteristics on the behavior of connection were studied and the associated load strain relationship and corre-sponding failure modes are presented. In this respect, an estimation of SPRBCCs moment and rotation are highly recommended in early stages of design and construction. In this study, a new approach based on Support Vector Machines (SVMs) coupled with discrete wavelet transform (DWT) is designed and adapted to estimate SPRBCCs moment and rotation according to four input parameters (column thickness, depth of connector and load, beam depth,). Results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. Following the results, SVM-WAVELET algorithm is helpful in order to enhance the accuracy compared to GP and ANN. It was conclusively observed that application of SVM-WAVELET is especially promising as an alternative approach to estimate the SPRBCCs moment and rotation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1252-1271
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• 2013

Automatic image annotation has become an increasingly important research topic owing to its key role in image retrieval. Simultaneously, it is highly challenging when facing to large-scale dataset with large variance. Practical approaches generally rely on similarity measures defined over images and multi-label prediction methods. More specifically, those approaches usually 1) leverage similarity measures predefined or learned by optimizing for ranking or annotation, which might be not adaptive enough to datasets; and 2) predict labels separately without taking the correlation of labels into account. In this paper, we propose a method for image annotation through collaborative similarity metric learning from dataset and modeling the label correlation of the dataset. The similarity metric is learned by simultaneously optimizing the 1) image ranking using structural SVM (SSVM), and 2) image annotation using correlated label propagation, with respect to the similarity metric. The learned similarity metric, fully exploiting the available information of datasets, would improve the two collaborative components, ranking and annotation, and sequentially the retrieval system itself. We evaluated the proposed method on Corel5k, Corel30k and EspGame databases. The results for annotation and retrieval show the competitive performance of the proposed method.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2003.10a
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• pp.133-138
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• 2003

The prediction of protein secondary structure has been an important bioinformatics tool that is an essential component of the template-based protein tertiary structure prediction process. It has been known that the predicted secondary structure information improves both the fold recognition performance and the alignment accuracy. In this paper, we describe several novel ideas that may improve the prediction accuracy. The main idea is motivated by an observation that the protein's structural information, especially when it is combined with the evolutionary information, significantly improves the accuracy of the predicted tertiary structure. From the non-redundant set of protein structures, we derive the 'potential' parameters for the protein secondary structure prediction that contains the structural information of proteins, by following the procedure similar to the way to derive the directional information table of GOR method. Those potential parameters are combined with the frequency matrices obtained by running PSI-BLAST to construct the feature vectors that are used to train the support vector machines (SVM) to build the secondary structure classifiers. Moreover, the problem of huge model file size, which is one of the known shortcomings of SVM, is partially overcome by reducing the size of training data by filtering out the redundancy not only at the protein level but also at the feature vector level. A preliminary result measured by the average three-state prediction accuracy is encouraging.

• ETRI Journal
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• v.40 no.4
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• Steel and Composite Structures
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• v.21 no.6
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• International Journal of Contents
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• v.13 no.2
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• pp.66-74
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• 2017

Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain's experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.