• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.275-285
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• 2012

DIFs (Data Interchange Formats) are needed to enhance interoperability of physically distributed organizations in SBA (Simulation Based Acquisition) process. DIFs play a role as a template of DPDs (Distributed Product Descriptions) and provide capability to use information directly without data format interchange process by allowing access to DPDs, which include various information and M&S (Modeling & Simulation) resources. This characteristic is essential for interoperability in ICE (Integrated Collaborative Environment) based SBA. This paper proposes a framework for the DIF and outputs from each phase of acquisition process for configuration data related to design and manufacturing in SBA process - Conceptual Data Model, Logical Data Model, Physical Data Model and Physical DIF based on XML. Finally, we propose the DIF model architecture and demonstrate the implementation of DIF example based on it.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.2
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• pp.327-333
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• 2014

The rapid expansion of the software industry has brought a serious security threat and vulnerability. Many softwares are constantly attacked by exploit codes using security vulnerabilities. Smart fuzzing is automated method to find software vulnerabilities. However, Many resources are consumed in fuzzing, because the fuzzing needs to create data model for target software and to analyze a data file and software binary. Therefore, The automated method for efficient smart fuzzing is needed to develop the automated data model. In this paper, through analysing the input file format and optimizing the data structure, we propose an efficient data modeling framework for smart fuzzing and implement the framework for detect software vulnerabilities.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.155-161
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• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.621-626
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• 2019

The driver's steering wheel maneuver is a typical disturbance that causes excessive body motion and traveling instability of a vehicle. Abrupt and extreme operation can cause rollover depending on the geometric and dynamic characteristics, e.g., SUV vehicles. In this study, to cope with the performance limitation of conventional cars, fundamental research on the structurization of a control system was performed as follows. Mathematical modeling of the lateral behavior induced by driver input was carried out. A controller was designed to reduce the body motion based on this model. An algorithm was applied to secure robust control performance against modeling errors due to parameter uncertainty, $H_{\infty}$. Using the decoupled 1/4 car, a dynamic load simulating model considering the body moment was suggested. The simulation result showed the validity of the load-simulating model. The framework for a lateral behavior control system is proposed, including an experimental 1/4 vehicle unit, load simulating module, suspension control module, and hardware-in-the-loop simulation technology.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.73-79
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• 2022

The Python language widely used in artificial intelligence (AI) projects is an interpreter language, and errors occur at runtime. In order to prevent project failure due to errors, it is necessary to handle exceptions in code that can cause exceptional situations in advance. In particular, in AI projects that require a lot of resources, exceptions that occur after long execution lead to a large waste of resources. However, since exception handling depends on the developer's experience, developers have difficulty determining the appropriate exception to catch. To solve this need, we propose an approach that recommends exceptions to catch to developers during development by learning the existing exception handling statements. The proposed method receives the source code of the try block as input and recommends exceptions to be handled in the except block. We evaluate our approach for a large project consisting of two frameworks. According to our evaluation results, the average AUPRC is 0.92 or higher when performing exception recommendation. The study results show that the proposed method can support the developer's exception handling with exception recommendation performance that outperforms the comparative models.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.4
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• pp.357-362
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• 2010

This paper proposes the speech and noise recognition system by using a neural network in order to detect the speech and noise sections at each frame. The proposed neural network consists of a layered neural network training by back-propagation algorithm. First, a power spectrum obtained by fast Fourier transform and linear predictive coefficients are used as the input to the neural network for each frame, then the neural network is trained using these power spectrum and linear predictive coefficients. Therefore, the proposed neural network can train using clean speech and noise. The performance of the proposed recognition system was evaluated based on the recognition rate using various speeches and white, printer, road, and car noises. In this experiment, the recognition rates were 92% or more for such speech and noise when training data and evaluation data were the different.

• The Journal of the Acoustical Society of Korea
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• v.41 no.2
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• pp.115-121
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• 2022

In this paper, we propose a music classification system according to user emotions using Electroencephalogram (EEG) features that appear when listening to music. In the proposed system, the relationship between the emotional EEG features extracted from EEG signals and the auditory features extracted from music signals is learned through a deep regression neural network. The proposed system based on the regression model automatically generates EEG features mapped to the auditory characteristics of the input music, and automatically classifies music by applying these features to an attention-based deep neural network. The experimental results suggest the music classification accuracy of the proposed automatic music classification framework.

• Journal of the Korean GEO-environmental Society
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• v.18 no.2
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• pp.47-51
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• 2017

In this study, the optimization algorithm by inverse analysis that is the core of the adaptive management technique was adopted to update the soil engineering properties based on the ground response during the construction. Adaptive management technique is the framework wherein construction and design procedures are adjusted based on observations and measurements made as construction proceeds. To evaluate the performance of the adaptive management technique, the numerical simulation for the triaxial tests and the synthetic deep excavation were conducted with the Hardening Soil model. To effectively conduct the analysis, the effective parameters among the parameters employed in the model were selected based on the composite scaled sensitivity analysis. The results from the undrained triaxial tests performed with soft Chicago clays were used for the parameter calibration. The simulation for the synthetic deep excavation were conducted assuming that the soil engineering parameters obtained from the triaxial simulation represent the actual field condition. These values were used as the reference values. The observation for the synthetic deep excavation simulations was the horizontal displacement of the support wall that has the highest composite scaled sensitivity among the other possible observations. It was found that the horizontal displacement of the support wall with the various initial soil properties were converged to the reference displacement by using the adaptive management technique.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.25-35
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• 2006

Standardization or standard-related study are regarded as main issues in GIS applications. Though several GIS standards and specifications have been released, there are a few actual application cases adapting those. In this study, we designed and implemented a geo-spatial information processing system with editing, storing, and disseminating functions, in which standard GIS transportation data model by TTA linked with OGC-GML, XML-based geographic features encoding standard. The system developed in this study enables us to transfer and edit transportation entities based on TTA standards to GML, importing ESRI shapefile. In web-based system, GML-based databases are transformed to SVG file, for the purpose of web publishing. TTA GIS transportation data model is used in this study, and tested; however, standard data models from other application fields also can be easily applied because this system basically provides data importing and editing functions. This system as practical tools can be utilized for applicability test of GIS standard data model and practical operation of standard specification.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.4
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• pp.249-256
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• 2019

This paper presents an optimization framework of electrical impedance tomography for characterizing electrical conductivity profiles of concrete structures in two dimensions. The framework utilizes a partial-differential-equation(PDE)-constrained optimization approach that can obtain the spatial distribution of electrical conductivity using measured electrical potentials from several electrodes located on the boundary of the concrete domain. The forward problem is formulated based on a complete electrode model(CEM) for the electrical potential of a medium due to current input. The CEM consists of a Laplace equation for electrical potential and boundary conditions to represent the current inputs to the electrodes on the surface. To validate the forward solution, electrical potential calculated by the finite element method is compared with that obtained using TCAD software. The PDE-constrained optimization approach seeks the optimal values of electrical conductivity on the domain of investigation while minimizing the Lagrangian function. The Lagrangian consists of least-squares objective functional and regularization terms augmented by the weak imposition of the governing equation and boundary conditions via Lagrange multipliers. Enforcing the stationarity of the Lagrangian leads to the Karush-Kuhn-Tucker condition to obtain an optimal solution for electrical conductivity within the target medium. Numerical inversion results are reported showing the reconstruction of the electrical conductivity profile of a concrete specimen in two dimensions.