• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.12
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• pp.2374-2391
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• 2011

A variety of reconstruction methods has been developed to convert a set of scattered points generated from real models into explicit forms, such as polygonal meshes, parametric or implicit surfaces. In this paper, we present a method to construct multi-scale implicit surfaces from scattered points using multiscale kernels based on kernel and multi-resolution analysis theories. Our approach differs from other methods in that multi-scale reconstruction can be done without additional manipulation on input data, calculated functions support level of detail representation, and it can be naturally expanded for n-dimensional data. The method also works well with point-sets that are noisy or not uniformly distributed. We show features and performances of the proposed method via experimental results for various data sets.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.768-774
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• 2010

Reconstruction of implicit surfaces from scattered point data sets have been developed in various engineering and scientific studies. In this paper, we represent a method to construct functions of 2D point data using multi-scale kernels and show it can be applied to graphics applications needed to access data in real-time. Our approach is similar to previous work in that a set of coefficients of the functions are calculated and stored in the preprocessing stage and function values at arbitrary positions are evaluated for real-time applications, however, it is different from others in that users can choose detail levels freely in real-time processing stage. The reason why the functions implicitly supports multi-resolution results from the mathematical properties of multi-scale kernels, and proposed method can be expanded to represent multi-resolution functions of n-dimensional data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1431-1445
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• 2022

We construct a deep neural network model named ECGResNet. This model can diagnosis diseases based on 12-lead ECG data of eight common cardiovascular diseases with a high accuracy. We chose the 16 Blocks of ResNet50 as the main body of the model and added the Squeeze-and-Excitation module to learn the data information between channels adaptively. We modified the first convolutional layer of ResNet50 which has a convolutional kernel of 7 to a superposition of convolutional kernels of 8 and 16 as our feature extraction method. This way allows the model to focus on the overall trend of the ECG signal while also noticing subtle changes. The model further improves the accuracy of cardiovascular and cerebrovascular disease classification by using a fully connected layer that integrates factors such as gender and age. The ECGResNet model adds Dropout layers to both the residual block and SE module of ResNet50, further avoiding the phenomenon of model overfitting. The model was eventually trained using a five-fold cross-validation and Flooding training method, with an accuracy of 95% on the test set and an F1-score of 0.841.We design a new deep neural network, innovate a multi-scale feature extraction method, and apply the SE module to extract features of ECG data.