Although there have been other policies interacting with the impact of GCP, GCP reforms implemented in the pilot districts at different times (as well as the later, standardized GCP system) have been effective in enabling CHCs to focus on providing quality public health services and appropriate medical treatment, rather than concentrating upon profit and loss. The impact of the standardized GCP system was further confirmed by cross-sectional comparisons of some broad indicators, in terms of medical cost, quality of medical service, and coverage of public health service, between the pilot districts and control districts. However, uncertainties exit when looking at individual indicators. Some indicators (see pp. 11-13 and Table 5), such as the service contracting rate with CHCs and the proportion of residents with health records set up, were not sufficient to allow for reasonable estimation of the impact of the GCP. In part this was due to inconsistent data collections. Some indicators, on the other hand, such as the standard management rate of residents with hypertension, were usually affected by the changing government's role over the period. Meanwhile, variations among the three pilot districts with different socioeconomic profiles were observed by several individual indicators within the evaluation index. Further research is needed to investigate the impact of other solutions--such as user fee removal and "zero margin profit" of medicine in CHCs--in order to coordinate other policies with the GCP to improve CHCs more effectively. Longer term observation of impact of the standardized GCP system, as well as other influencing factors in Shanghai based on quality data collected on a standard basis, may help improve policy. Moreover, variations in residents' expectations of barriers in access to CHC services and in healthcare-seeking behavior need to be taken into consideration when designing GCP systems for areas with different socioeconomic profiles in order to meet the different health needs which are a consequence of the major socioeconomic changes in Shanghai (and China in general, it could be agreed).

SummaryIn order to deal with the overestimation of matched uncertainty and improve the convergence of sliding variable in sliding mode control, a modified structure of super‐twisting algorithm (STA) with inner feedback and adaptive gain schedule is presented in this paper. The foremost characteristic of the modified STA is that an inner feedback mechanism is built in the standard STA so as to regulate the dynamic behavior of sliding variable effectively. The damping effect produced by the inner feedback can restrain the overshoot and enhance the performance of faster convergence of the sliding variable. Furthermore, the adaptive gain schedule can effectively decrease the chattering amplitude without knowing the upper bound of uncertainty. The numerical simulations and experiments on DC servo system with low speed are carried out to validate the effectiveness and performance advantages of the proposed methodology. Copyright © 2016 John Wiley & Sons, Ltd.

Semantic segmentation on high-resolution aerial images plays a significant role in many remote sensing applications. Although the Deep Convolutional Neural Network (DCNN) has shown great performance in this task, it still faces the following two challenges: intra-class heterogeneity and inter-class homogeneity. To overcome these two problems, a novel dual-path DCNN, which contains a spatial path and an edge path, is proposed for high-resolution aerial image segmentation. The spatial path, which combines the multi-level and global context features to encode the local and global information, is used to address the intra-class heterogeneity challenge. For inter-class homogeneity problem, a Holistically-nested Edge Detection (HED)-like edge path is employed to detect the semantic boundaries for the guidance of feature learning. Furthermore, we improve the computational efficiency of the network by employing the backbone of MobileNetV2. We enhance the performance of MobileNetV2 with two modifications: (1) replacing the standard convolution in the last four Bottleneck Residual Blocks (BRBs) with atrous convolution; and (2) removing the convolution stride of 2 in the first layer of BRBs 4 and 6. Experimental results on the ISPRS Vaihingen and Potsdam 2D labeling dataset show that the proposed DCNN achieved real-time inference speed on a single GPU card with better performance, compared with the state-of-the-art baselines.by these methods are not good at discriminating: (1) two objects which are classified into the same semantic label but with different appearances, named intra-class heterogeneity, as shown in Figure 1a, where the houses (or cars) have different shapes, sizes, and colors, but they belong to the same semantic label; and (2) two adjacent objects which are categorized into two different semantic labels but with similar appearances, named inter-class homogeneity, as shown in Figure 1b, where the low vegetation and trees are similar in colors, but their semantic labels are distinct. To tackle these two challenges, we need to consider each category of pixels as a whole, instead of assigning semantic label to each single pixel independently. To address the intra-class heterogeneity issue, we need to combine the multi-level and global context features to encode the local and global information, which can learn the discriminative and effective features to correctly categorize variant objects belonged to the same semantic label. Semantic boundaries can detect the feature variations on adjacent objects with similar appearance but different semantic labels. We can integrate it into the training process to help the network to learn the discriminative features to enlarge the inter-class differences. Based on the above two points, we propose a novel Deep Convolutional Neural Network (DCNN) that contains a spatial path and an edge path to tackle the problems of intra-class heterogeneity and inter-class homogeneity in high-resolution aerial images simultaneously.(a) intra-class heterogeneity (b) inter-class homogeneity

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In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

Background subtraction (BS) is one of the most commonly encountered tasks in video analysis and tracking systems. It distinguishes the foreground (moving objects) from the video sequences captured by static imaging sensors. Background subtraction in remote scene infrared (IR) video is important and common to lots of fields. This paper provides a Remote Scene IR Dataset captured by our designed medium-wave infrared (MWIR) sensor. Each video sequence in this dataset is identified with specific BS challenges and the pixel-wise ground truth of foreground (FG) for each frame is also provided. A series of experiments were conducted to evaluate BS algorithms on this proposed dataset. The overall performance of BS algorithms and the processor/memory requirements were compared. Proper evaluation metrics or criteria were employed to evaluate the capability of each BS algorithm to handle different kinds of BS challenges represented in this dataset. The results and conclusions in this paper provide valid references to develop new BS algorithm for remote scene IR video sequence, and some of them are not only limited to remote scene or IR video sequence but also generic for background subtraction. The Remote Scene IR dataset and the foreground masks detected by each evaluated BS algorithm are available online: .

Purpose This paper aims to present an underwater climbing robot for wiping off marine life from steel pipes (e.g. jackets of oil platforms). The self-adaption mechanism that consists of a passive roll joint and combined magnet adhesion units provides the robot with better mobility and stability. Design/methodology/approach Adhesion requirements are achieved by analyses of falling and slipping. The movement status on pipes is analyzed to design the passive roll joint. The optimized structure parameters of the combined magnet adhesion unit are achieved by simulations. An approximation method is established to simplify the simulations conditions, and the simulations are conducted in two steps to save time effectively. Findings The self-adaption mechanism has expected performance that the robot can travel on pipes in different directions with high mobility. Meanwhile, the robot can clean continuous region of underwater pipes’ surface of offshore platforms. Practical implications The proposed underwater robot is needed by offshore oil platforms as their jackets require to be cleaned periodically. Compared with traditional maintenance by divers, it is more efficient, economic and safety. Originality/value Due to the specific self-adaption mechanism, the robot has good mobility and stability in any directions on pipes with different diameters. The good performance of striping attachments from pipes makes the underwater robot be a novel solution to clean steel pipes.

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