Summary This paper proposes an identification framework based on a restricted Boltzmann machine (RBM) for crack identification and extraction from images containing cracks and complicated background inside steel box girders of bridges. The original images that include fatigue crack and other background information are obtained by a consumer‐grade camera inside the steel box girder. The original images are cut into a number of elements with small size as the input dataset, and a state representation vector is artificially labeled to every image element used for the crack identification. A deep learning model or network consisting of multiple processing RBM layers to learn the abstract features is constructed to match the input image elements with corresponding state representation vectors. Next, a three‐layer RBM with 500; 500; and 2,000 hidden units is trained as the hidden layers in the deep learning network. A contrastive divergence learning algorithm is employed for training the deep network to update and obtain the optimal parameters (i.e., the biases and weights). The new input image elements labeled as crack are sorted out and assembled to form an output image. A deep network is modeled through the consumer‐grade camera images containing cracks and complicated background information using the proposed approach. The accuracy and ability to identify cracks from new images with different resolutions using the trained deep network are validated. Furthermore, effects of element size on reconstruction error and identification accuracy are investigated. The results show that there exists optimal element size; that is, too small and too large element sizes both increase the reconstruction error and decrease the identification accuracy.

This paper intends to investigate the effectiveness of a new course pattern-TED-motivated English Public Speaking Course in EFL teaching in China. This class framework adopts TED videos as the learning materials to stimulate students to be a better speaker. Meanwhile, it aims to examine to what extent the five aspects of language skills are improved. Participants are required to give answers to the questions in the questionnaires. SPSS 15.0 is used to analyze the data. The result shows that students in this course respond very positively to this new pattern and are satisfied with their improvements in language skills; they have shifted their roles from a learner to a creator; their critical listening and thinking abilities are greatly enhanced at the same time.

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