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To reduce the dependence on fossil fuel and imported energy resources, Taiwan has ever-increasing needs of renewable energy. With the rapid development of the technologies of wave energy converter, the wave energy source will be able to meet parts the demand. The Energy Research Laboratories of the Industrial Technology Research Institute, Taiwan (2005), based on the statistic of one-year wave data, stated that the mean wave energy at the northeast coast of Taiwan reaches 11.56 kW/m, giving it the potential of wave power utilization. However, one of the major obstacles with the wave energy utilization is lack of long-term ocean wave measurements. The long-term variations in wave parameters impose changes in wave energy converter outputs. Lack of long-term data makes it difficult to assess the cost-benefit of wave energy conversion projects for the policy and decision makers. The present study aims to quantitatively evaluate the wave climate variations of the northwestern Pacific and the Taiwan Waters based on long-term wave data base. Wave observations around Taiwan have been performed since 1998, thus, earlier data of wave climate are not available. This study reconstructs the wave data of the northwest Pacific over the past three decades based on the SWAN numerical wave model that driven by NECP global reanalysis wind fields. The simulation results are compared and validated with measured data. The results show that the long-term wave climate variations around Taiwan consist of oscillations of three different periods, i.e. the seasonal, inter-annual and decade oscillations. The seasonal oscillation has significant amplitude that leads the wave energy one order magnitude greater in winter than in summer. In addition to seasonal changes, the wave energy features inter-annual variations, which are highly related to the El Nino and La Nina phenomena. In the La Nina years, the annual averaged wave energy could be double than in El Nino years. Finally, this study adopted the Man-Kendall Non-Parametric Test and the Hilbert Huang EMD method to analyze the long-term wave variation trends. The results showed that the wave height experienced climbing trends during 1976-1985 and 1997-2006, and a descending trend during 1985-1997. The reasons for wave climate oscillations in the decadal variation should be further investigation. (C) 2011 Elsevier Ltd. All rights reserved

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China's carbon emission trading market has been formally established, but few studies have been conducted to analyze the impact of this policy on the regional urbanization level. Therefore, this paper evaluates whether the carbon trading pilot policy can enhance the regional urbanization level in China through the difference-in-differences method and analyzes the mediating role of industrial structure upgrading in this process. The results prove that the carbon trading market policy can accelerate the transformation and upgrading of industrial structure in the region so that it promotes the development of regional urbanization. Moreover, the effects of the policy are concentratedly manifested in the eastern region of China.

Since light propagation in water bodies is subject to absorption and scattering effects, underwater images using only conventional intensity cameras will suffer from low brightness, blurred images, and loss of details. In this paper, a deep fusion network is applied to underwater polarization images; that is, the underwater polarization images are fused with intensity images using the deep learning method. To construct a training dataset, we establish an experimental setup to obtain underwater polarization images and perform appropriate transformations to expand the dataset. Next, an end-to-end learning framework based on unsupervised learning and guided by an attention mechanism is constructed for fusing polarization and light intensity images. The loss function and weight parameters are elaborated. The produced dataset is used to train the network under different loss weight parameters, and the fused images are evaluated based on different image evaluation metrics. The results show that the fused underwater images are more detailed. Compared with light intensity images, the information entropy and standard deviation of the proposed method increase by 24.48% and 139%. The image processing results are better than other fusion-based methods. In addition, the improved U-net network structure is used to extract features for image segmentation. The results show that the target segmentation based on the proposed method is feasible under turbid water. The proposed method does not require manual adjustment of weight parameters, has faster operation speed, and has strong robustness and self-adaptability, which is important for research in vision fields, such as ocean detection and underwater target recognition.