• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1170-1178
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• 2021

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.211-214
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• 2007

With the recent development of spatial information technology, the relative importance of satellite image contents has increased to about 62%, the techniques related to satellite images have improved, and their demand is gradually increasing. Accordingly, a standard processing method for the whole process of collection from satellites to distribution of satellite images is required in many countries for efficient distribution of images and improvement of their utilization. This study presents the processor standardization technique for the preprocessing of satellite images including geometric correction, orthorectification, color adjustment, interpolation for DEM (Digital Elevation Model) production, rearrangement, and image data management, which will standardize the subjective, complex process and improve their utilization by making it easy for general users to use them

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.687-692
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• 2024

The energy crisis is emerging as a serious problem around the world. In the case of Korea, there is great interest in energy efficiency research related to industrial complexes, which use more than 53% of total energy and account for more than 45% of greenhouse gas emissions in Korea. One of the studies is a study on saving energy through sharing facilities between factories using the same utility in an industrial complex called a virtual energy network plant and through transactions between energy producing and demand factories. In such energy-saving research, data collection is very important because there are various uses for data, such as analysis and prediction. However, existing systems had several shortcomings in reliably collecting time series data. In this study, we propose an intelligent IIoT platform to improve it. The intelligent IIoT platform includes a preprocessing system to identify abnormal data and process it in a timely manner, classifies abnormal and missing data, and presents interpolation techniques to maintain stable time series data. Additionally, time series data collection is streamlined through database optimization. This paper contributes to increasing data usability in the industrial environment through stable data collection and rapid problem response, and contributes to reducing the burden of data collection and optimizing monitoring load by introducing a variety of chatbot notification systems.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.4
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• pp.21-26
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• 2011

Feature ranking is useful to gain knowledge of data and identify relevant features. In this study, we proposed a use of feature ranking for classification of neuro-degeneration and vascular dementia in micro-Raman spectra of platelet. The entire region of the spectrum is divided into local region including several peaks, followed by Gaussian curve fitting method in the region to be modeled. Local minima select from the subregion and then remove the background based on the position by using interpolation method. After preprocessing steps, significant features were selected by feature ranking method to improve the classification accuracy and the computational complexity of classification system. PCA (principal component analysis) transform the selected features and the overall features that is used classification with the number of principal components. These were classified as MAP (maximum a posteriori) and it compared with classification result using overall features. In all experiments, the computational complexity of the classification system was remarkably reduced and the classification accuracy was partially increased. Particularly, the proposed method increased the classification accuracy in the experiment classifying the Parkinson's disease and normal with the average 1.7 %. From the result, it confirmed that proposed method could be efficiently used in the classification system of the neuro-degenerative disease and vascular dementia of platelet.

• Journal of Korea Game Society
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• v.7 no.1
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• pp.31-42
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• 2007

In the natural world, most materials such as skin, marble and cloth are translucent. Their appearance is smooth and soft compared with metals or mirrors. In this paper, we propose a new GPU based hierarchical rendering technique for translucent materials, based on the dipole diffusion approximation, at interactive rates. Information of incident light, position, normal, and irradiance, on the surfaces are stored into 2D textures by rendering from a primary light view. Huge numbers of pixel photons are clustered into quad-tree image pyramids. Each pixel, we select clusters (sets of photons), and then we approximate multiple subsurface scattering term with the clusters. We also introduce a novel hierarchical screen-space interpolation technique by exploiting spatial coherence with early-z culling on the GPU. We also build image pyramids of the screen using mipmap and pixel shader. Each pixel of the pyramids is stores position, normal and spatial similarity of children pixels. If a pixel's the similarity is high, we render the pixel and interpolate the pixel to multiple pixels. Result images show that our method can interactively render deformable translucent objects by approximating hundreds of thousand photons with only hundreds clusters without any preprocessing. We use an image-space approach for entire process on the GPU, thus our method is less dependent to scene complexity.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.489-497
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• 1994

The most serious problems related to the cardiovascular prothesis are thrombosis and hemolysis. It is known that the flow pattern of cardiovascular prostheses is highly correlated with thrombosis and hemolysis. Laser Doppler Anemometry (LDA) is a usual method to get flow pattern, which is difficult to operate and has narrow measure region. Particle Image Velocimetry (PIV) can solve these problems. Because the flow speed of valve is too high to catch particles by CCD camera, high-speed camera (Hyspeed : Holland-Photonics) was used. The estimated maximum flow speed was 5m/sec and maximum trackable length is 0.5 cm, so the shutter speed was determined as 1000 frames per sec. Several image processing techniques (blurring, segmentation, morphology, etc) were used for the preprocessing. Particle tracking algorithm and 2-D interpolation technique which were necessary in making gridrized velocity pronto, were applied to this PIV program. By using Single-Pulse Multi-Frame particle tracking algorithm, some problems of PIV can be solved. To eliminate particles which penetrate the sheeted plane and to determine the direction of particle paths are these solving methods. 1-D relaxation fomula is modified to interpolate 2-D field. Parachute artificial heart valve which was developed by Seoul National University and Bjork-Shiely valve was testified. For each valve, different flow pattern, velocity profile, wall shear stress and mean velocity were obtained.

• The KIPS Transactions:PartB
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• v.15B no.5
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• pp.477-486
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• 2008

Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. To improve the face recognition rate, we propose a normalization method of facial expression to diminish the difference of facial expression between probe and gallery faces. Two approaches are used to facial expression modeling and normalization from single still images using a generic facial muscle model without the need of large image databases. The first approach estimates the geometry parameters of linear muscle models to obtain a biologically inspired model of the facial expression which may be changed intuitively afterwards. The second approach uses RBF(Radial Basis Function) based interpolation and warping to normalize the facial muscle model as unexpressed face according to the given expression. As a preprocessing stage for face recognition, these approach could achieve significantly higher recognition rates than in the un-normalized case based on the eigenface approach, local binary patterns and a grey-scale correlation measure.