• Journal of KIISE:Software and Applications
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• v.36 no.1
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• pp.82-91
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• 2009

Sign language spotting is the task of detecting and recognizing the signs in a signed utterance. The difficulty of sign language spotting is that the occurrences of signs vary in both motion and shape. Moreover, the signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and non-sign patterns(which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing a threshold model in a conditional random field(CRF) model is proposed. The proposed model performs an adaptive threshold for distinguishing between signs in the vocabulary and non-sign patterns. A hand appearance-based sign verification method, a short-sign detector, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experimental results show that the proposed method can detect signs from continuous data with an 88% spotting rate and can recognize signs from isolated data with a 94% recognition rate, versus 74% and 90% respectively for CRFs without a threshold model, short-sign detector, subsign reasoning, and hand appearance-based sign verification.

• Journal of the Korea Society for Simulation
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• v.23 no.1
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• pp.1-10
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• 2014

A cloud computing is one of a parallel and distributed computing. The cloud computing provides some service for user with virtual resources. However, a user's service request does not show a time pattern. As a result, each resource also shows a different availability at the same time. This difference affects a quality of service (QoS) and a resource selection for users. Therefore, we propose the resource availability-based multi auction model for cloud service reservation and resource brokering system. The proposed system is to select the proper resource provider based on the users' request. The proposal adopts the multi phase of the auction to transact resources. The system evaluates the available factor of each resource on the auction phase, and finally reserves the service on the adaptive queue. The proposed model shows the better performance than other existing method.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.488-493
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• 2015

An AddSIM(Adaptive distributed and parallel Simulation environment for Interoperable and reusable Models) is an integrated engagement simulation environment with high-resolution weapon system models for estimation and analysis of their performance and effectiveness. AddSIM can simultaneously handle the continuous dynamical system models based on continuous time, and command, control(C2) and network system models based on a discrete event. To accommodate legacies based on DEVS(Discrete Event System Specification) modeling, DEVS legacies must first be converted into AddSIM models. This paper describes how to implement DEVS models on AddSIM. In this study a method of mapping from hierarchical DEVS models to AddSIM players was developed: The hierarchical DEVS model should be flattened into a one layered model and four DEVS functions of the model, external transition, internal transition, output and time advance, should be mapped into functions of the AddSIM player.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.11
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• pp.69-78
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• 2019

The purpose of this study is to propose a modeling methodology through the exchange of coordinate data of a three-dimensional custom curtain wall panel between Rhino and Revit, and to examine the validity of the model implemented in the drawing. Although the modeling means and method are different, a fundamental principle is that all three-dimensional modeling begins by defining the position of the points, the most primitive element of geometry, in the XYZ coordinate space. For the BIM modeling methodology proposal based on this geometry basic concept, the functions and characteristics associated with the points of Rhino and Revit programs are identified, and then BIM implementation process model is organized and systemized through the setting of the interoperability process algorithm. The BIM implementation process model proposed in this study is (1) Modeling and panelizing surface into individual panels using Rhino and Grasshopper; (2) Extraction of vertex coordinate data from individual panels and create CSV file; (3) Curtain wall modeling through Adaptive Component Family in Revit and (4) Automatic creation of Revit curtain wall panels through API. The proposed process model is expected to help reduce design errors and improve component and construction quality by automatically converting general elements into architectural meaningful information, automating a set of processes that build them into BIM data, and enabling consistent and integrated design management.

• Journal of Biomedical Engineering Research
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• v.42 no.3
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• pp.80-85
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• 2021

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.

• The Journal of Korean Medicine
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• v.43 no.2
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• pp.27-41
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• 2022

Objectives: There should be an objective analysis on the academic competency for incorporating Computer-based Test (CBT) in the education of traditional Korean medicine (TKM). However, the Item Response Theory (IRT) for analyzing latent competency has not been introduced for its difficulty in calculation, interpretation and utilization. Methods: The current study analyzed responses of 390 students of 8 years to the herbology test with 14 items by utilizing Rasch model, and the characteristics of test and items were evaluated by using characteristic curve, information curve, difficulty, academic competency, and test score. The academic competency of the students across gender and years were presented with scale characteristic curve, Kernel density map, and Wright map, and examined based on T-test and ANOVA. Results: The estimated item, test, and ability parameters based on Rasch model provided reliable information on academic competency, and organized insights on students, test and items not available with test score calculated by the summation of item scores. The test showed acceptable validity for analyzing academic competency, but some of items revealed difficulty parameters to be modified with Wright map. The gender difference was not distinctive, however the differences between test years were obvious with Kernel density map. Conclusion: The current study analyzed the responses in the herbology test for measuring academic competency in the education of TKM using Rasch model, and structured analysis for competency-based Teaching in the e-learning era was suggested. It would provide the foundation for the learning analytics essential for self-directed learning and competency adaptive learning in TKM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.259-268
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• 2007

This paper contributes to an improvement of the statistical bandwidth extension(BWE) system based on Hidden Markov Model(HMM). First, the existing HMM training method for BWE, which is suggested originally by Jax, is analyzed in comparison with the general Baum-Welch training method. Next, based on this analysis, a new HMM-based BWE method is suggested which adopts the Baum-Welch re-estimation algorithm instead of the Jax's to train HMM model. Conclusionally speaking, the Baum-Welch re-estimation algorithm is a generalized form of the Jax's training method. It is flexible and adaptive in modeling the statistical characteristic of training data. Therefore, it generates a better model to the training data, which results in an enhanced BWE system. According to experimental results, the new method performs much better than the Jax's BWE systemin all cases. Under the given test conditions, the RMS log spectral distortion(LSD) scores were improved ranged from 0.31dB to 0.8dB, and 0.52dB in average.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2343-2351
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• 2024

Accurately predicting the thermal hydraulic parameters of a transient reactor core under different working conditions is the first step toward reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which have often been modeled as time series prediction problems. This study aims to achieve accurate and continuous prediction of core thermal hydraulic parameters under instantaneous conditions, as well as test the feasibility of a newly constructed gated recurrent unit (GRU) model based on the soft attention mechanism for core parameter predictions. Herein, the China Experimental Fast Reactor (CEFR) is used as the research object, and CEFR 1/2 core was taken as subject to carry out continuous predictive analysis of thermal parameters under transient conditions., while the subchannel analysis code named SUBCHANFLOW is used to generate the time series of core thermal-hydraulic parameters. The GRU model is used to predict the mass flow and temperature time series of the core. The results show that compared to the adaptive radial basis function neural network, the GRU network model produces better prediction results. The average relative error for temperature is less than 0.5 % when the step size is 3, and the prediction effect is better within 15 s. The average relative error of mass flow rate is less than 5 % when the step size is 10, and the prediction effect is better in the subsequent 12 s. The GRU model not only shows a higher prediction accuracy, but also captures the trends of the dynamic time series, which is useful for maintaining reactor safety and preventing nuclear power plant accidents. Furthermore, it can provide long-term continuous predictions under transient reactor conditions, which is useful for engineering applications and improving reactor safety.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.62-74
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• 2002

The tracking of a reference temperature trajectory in a polymerization batch reactor is a common problem and has critical importance because the quality control of a batch reactor is usually achieved by implementing the trajectory precisely. In this study, only energy balances around a reactor are considered as a design model for control synthesis, and material balances describing concentration variations of involved components are treated as unknown disturbances, of which the effects appear as time-varying parameters in the design model. For the synthesis of a tracking controller, a method combining the input-output linearization of a time-variant system with the parameter estimation is proposed. The parameter estimation method provides parameter estimates such that the estimated outputs asymptotically follow the measured outputs in a specified way. Since other unknown external disturbances or uncertainties can be lumped into existing parameters or considered as another separate parameters, the method is useful in practices exposed to diverse uncertainties and disturbances, and the designed controller becomes robust. And the design procedure and setting of tuning parameters are simple and clear due to the resulted linear design equations. The performances and the effectiveness of the proposed method are demonstrated via simulation studies.