• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.97-105
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• 2016

Network-Centric Operational Environment(NCOE) service becomes critical in today's military environment network because reusability of service and interaction are being increasingly important as well in business process. However, the refinement of service by semantic similarity and functional similarity at the business process was not detailed yet. In order to enhance accuracy of refining of business service, in this study, the authors introduce a method for refining service by semantic similarity and functional similarity in BPMN model. The business process are designed in a BPMN model. In this model, candidated services are refined through binding related activities by the analysis result of semantic similarity based on word-net and functional similarity based on properties specification between activities. Then, the services are identified through refining the candidated service. The proposed method is expected to enhance the service identification with accuracy and modularity. It also can accelerate more standardized service refinement developments by the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2101-2123
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• 2023

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

• Korean Journal of Computational Design and Engineering
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• v.16 no.1
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• pp.21-30
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• 2011

Similarity assessment of a CAD model is one of important issues from the aspect of model re-using. In real practice, many new mechanical parts are designed by modifying existing ones. The reuse of part enables to save design time and efforts for the designers. Design time would be further reduced if there were an efficient way to search for existing similar designs. This paper proposes an efficient algorithm of similarity assessment for mechanical part model with design history embedded within the CAD model. Since it is possible to retrieve the design history and detailed-feature information using CAD API, we can obtain an accurate and reliable assessment result. For our purpose, our assessment algorithm can be divided by two: (1) we select suitable parts by comparing MSG (Model Signature Graph) extracted from a base feature of the required model; (2) detailed-features' similarities are assessed with their own attributes and reference structures. In addition, we also propose a indexing method for managing a model database in the last part of this article.

• Journal of Ocean Engineering and Technology
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• v.15 no.3
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• pp.134-141
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• 2001

An experimental method to investigate the dynamic characteristics of buoys in extreme environmental condition is established. Because the buoy model requires a resonable size for accurate experiment, the test condition in model basin that satisfies the similarity law is hardly compatible with capability of test facilities. It is suggested that the linear wave component that is unable to satisfy similarity is separated with others. The model experiment is carried out with mitigated condition for the linear wave components while others including wave drift, current and wind are keeping the similarities. Then, the result can be extrapolated to give the dynamic behavior of buoys n extreme condition because linear wave component is solely responsibly to oscillatory buoy motion and other environmental components are applied as a initial tension. The similarity for current and wind conditions is viewed as equivalence of restoring forces. The validity of proposed method is examined with different types of standard ocean buoys and it indicates that the linearity of measured characteristics is assured with a limitation of resonable distance between test and estimated wave conditions.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2001.05a
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• pp.208-215
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• 2001

An experimental method to investigate the dynamic charasteristics of buoys in extreme environmental condition is established. Because the buoy model requires a resonable size for accurate experiment, the test condition in model basin that satisfies the similarity law is hardly met with capability of test facilities. It is suggested that the linear wave component that is unable to satisy similarity is separated with others. The model experiment can be carried out with mitigated condition for the linear wave components while others including wave drift, current and wind are keeping the similarities. Then the result is extrapolated to give the dynamic behavior of buoys in extreme condition because linear wave component is soley responsible to oscillatory buoy motion and other environmental components are applied as a initial tension. the similarity for current and wind conditions is viewed as equivalence of restoring forces. the validity of proposed method is examined with different types of standard ocean buoys and it indicates that the linearity of measured characteristics is assured with a limitation of resonable distance between test and estimated wave conditions.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.1
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• pp.10-16
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• 2015

In this study, we suggest a method to predict probability distribution of a new customer's degree of loyalty using C-CRF that reflects the RFM score and similarity to the neighbors of the customer. An RFM score prediction model is introduced to construct the first feature function of C-CRF. Integrating demographical similarity, purchasing characteristic similarity and purchase history similarity, we make a unified similarity variable to configure the second feature function of C-CRF. Then parameters of each feature function are estimated and we train our C-CRF model by training data set and suggest a probabilistic distribution to estimate a new customer's degree of loyalty. An example is provided to illustrate our model.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.313-318
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• 2004

When plan breakdown by permeation of fill dam, bank by original decision scale model test of sound, original decision scale model test of sound that destroy having used water was carried out. And original decision scale model test of sound that use viscous fluid is carried out, but doubt remains in experiment result in state that verification of law of similarity is not achieved. In this study, verified according to Modeling of Models' method effecting law of similarity to use n ship horoscope solution of water.

• Journal of Advanced Marine Engineering and Technology
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• v.34 no.2
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• pp.275-281
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• 2010

In this paper, a geometric similarity law is introduced to the performance test of a large-sized silencer used in ship engine or plant system. A test of scale-down model enable to yield the cost and time saving in developing large-sized silencer considerably. Two types of silencer, resonator and expansion chamber, were analyzed by a theoretical method and an acoustical FEM(finite element method) in order to obtain geometric similarity variables. A method is proposed to estimate the transmission loss of prototype model using the test results of scale-down model. Two actual large-sized silencer, which consist of resonator and expansion chamber, were analysed by an acoustical FE analysis. Consequently, the proposed method predicts effectively the performance of prototype silencers using those of scale-down models.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.99-105
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• 2011

We propose a semi-supervised learning algorithm based on a form of regularization that incorporates similarity and dissimilarity penalty terms. Our approach uses a graph-based encoding of similarity and dissimilarity. We also present a model-selection method which employs cross-validation techniques to choose hyperparameters which affect the performance of the proposed method. Simulations using two types of dat sets demonstrate that the proposed method is promising.