The early detection and rapid quantification of acute ischemic lesions play pivotal roles in stroke management. We developed a deep learning algorithm for the automatic binary classification of the Alberta Stroke Program Early Computed Tomographic Score (ASPECTS) using diffusion-weighted imaging (DWI) in acute stroke patients. Three hundred and ninety DWI datasets with acute anterior circulation stroke were included. A classifier algorithm utilizing a recurrent residual convolutional neural network (RRCNN) was developed for classification between low (1–6) and high (7–10) DWI-ASPECTS groups. The model performance was compared with a pre-trained VGG16, Inception V3, and a 3D convolutional neural network (3DCNN). The proposed RRCNN model demonstrated higher performance than the pre-trained models and 3DCNN with an accuracy of 87.3%, AUC of 0.941, and F1-score of 0.888 for classification between the low and high DWI-ASPECTS groups. These results suggest that the deep learning algorithm developed in this study can provide a rapid assessment of DWI-ASPECTS and may serve as an ancillary tool that can assist physicians in making urgent clinical decisions.

This paper presents an Assembly Design (AsD) ontology that explicitly represents AsD constraints and infers any remaining implicit ones. By relating concepts through ontology technology rather than just defining data syntax, assembly and joining concepts can be captured in their entirety or extended as necessary. Ontologies allow assembly and joining constraints to be represented in a standard manner regardless of geometry file formats. Such representation will significantly improve service-oriented design collaboration. The developed AsD ontology is tested using a realistic mechanical assembly and it is shown how the ontology can be used to capture design rationale and analyze the design intents. In conclusion, the significance of ontology for realizing lean and selective assembly design information sharing is discussed.

In this paper, a system for automatic detection and recognition of Korean texts or shop names in outdoor signboard images is described. The system includes detection, binarization and extraction of text in a signboard image captured by a camera of a mobile phone for the recognition of the shop name. It can deal with different font styles and sizes as well as illumination changes. Individual characters detected by connected component analysis are recognized by using nonlinear mesh, in which feature vectors of vertical and horizontal components are extracted from the binarized image. Proposed methods have been applied to a Korean text translation system, which can automatically detect and recognize Korean texts and generate the translation result.

Unsupervised mining of electrocardiography (ECG) time series is a crucial task in biomedical applications. To have efficiency of the clustering results, the prominent features extracted from preprocessing analysis on multiple ECG time series need to be investigated. In this paper, a Harmonic Linear Dynamical System is applied to discover vital prominent features via mining the evolving hidden dynamics and correlations in ECG time series. The discovery of the comprehensible and interpretable features of the proposed feature extraction methodology effectively represents the accuracy and the reliability of clustering results. Particularly, the empirical evaluation results of the proposed method demonstrate the improved performance of clustering compared to the previous main stream feature extraction approaches for ECG time series clustering tasks. Furthermore, the experimental results on real-world datasets show scalability with linear computation time to the duration of the time series.

ECG consists of various waveforms of electric signals of heat. Machine Learning methods such as the MLP classification have proven to perform well in ECG classification. In this study, preprocessing was performed through wavelet transform, and in classification several characteristics were evaluated using BP algorithm that applied generalized delta rules to MLP. In order to decide wavelet generating function that can remove baseline by minimizing the distortion of raw signals, this study removed baseline by applying various wavelet generating functions. To evaluate the results above according to the learning method, learning iteration and learning rate of neural networks, various experiments were conducted.