No abstract

Inkjet printing technology can make tiles with very rich and realistic patterns, so it is widely adopted in the ceramic industry. However, the frequent nozzle blockage and inconsistent inkjet volume by inkjet printing devices, usually leads to defects such as stayguy and color blocks in the tile surface. Especially, the stayguy in complex pattern is difficult to identify by naked eyes due to it is easily covered by complex patterns and becomes invisible, this brings great challenge to tile quality inspection. Nowadays, the machine learning is employed to address the issues. The existing machine learning methods based on hand‐crafted features are capable of stayguy detection of the tiles with a simple pattern, but not applicable for complex patterns due to the interference of pattern in feature extraction. The emerging deep‐learning‐based methods have the potential to be applied for stayguy detection with complex patterns, but cannot achieve real‐time detection due to high complexity. In this paper, a lightweight hand‐crafted feature enhanced convolutional neural network (named HFENet) is proposed for rapid defect detection of tile surface. First, we perform data enhancement on the original image by global histogram equalization and image addition. Second, for the special shape of stayguy which is usually vertical, we embed the extended vertical edge detection operator (Prewitt) as convolution kernel into HFENet to extract the hand‐crafted vertical edge features of the test image and eliminate the interference of complex pattern in the feature extraction. Third, the 5 × 1 asymmetric convolution kernel with a dilation rate of 2 is used to improve the utilization of convolution kernel and reduce the complexity of the model. Fourth, to reach the real‐time requirements, a memory access cost‐aware design is proposed, which can orchestrate the number of shallow convolution layers and deep convolution layers in feature extraction. The experiments were performed on the ceramic tile image data set captured by high‐resolution industrial cameras in ceramic tile production line. Experimental results show that the HFENet outperforms the state‐of‐the‐art semantic segmentation networks (i.e., UNet, FCN‐8s, SegNet, DeepLabV3+, etc.) and lightweight networks (i.e., ShuffleNet, MobileNet, and SqueezeNet). All the code and data are available at a GitHub repository (https://github.com/RobotvisionLab/HFENet).

Due to the incomplete coverage and failure of traffic data collectors during the collection, traffic data usually suffers from information missing. Achieving accurate imputation is critical to the operation of transportation networks. Existing approaches usually focus on the characteristic analysis of temporal variation and adjacent spatial representation, and the consideration of higher-order spatial correlations and continuous data missing attracts more attentions from the academia and industry. In this paper, by leveraging motif-based graph aggregation, we propose a spatiotemporal imputation approach to address the issue of traffic data missing. First, through motif discovery, the higher-order graph aggregation model was presented in traffic networks. It utilized graph convolution network (GCN) to polymerize the correlated segment attributes of the missing data segments. Then, the multitime dimension imputation model based on bidirectional long short-term memory (Bi-LSTM) incorporated the recent, daily-periodic, and weekly-periodic dependencies of the historical data. Finally, the spatial aggregated values and the temporal fusion values were integrated to obtain the results. We conducted comprehensive experiments based on the real-world dataset and discussed the case of random and continuous data missing by different time intervals, and the results showed that the proposed approach was feasible and accurate.

From the perspective of household economy, the application of a multiscale spatial econometric model to realize the objective evaluation of county-level poverty alleviation stability is a core issue in rural economics research. The improvement of economic income and livelihood conditions for small farm holders are significant manifestations of poverty alleviation stability. Quantitative evaluation of the county-level poverty alleviation stability can provide a scientific basis for the adjustment of rural economic policy and high-quality development of regional economy by the multiscale spatial econometric model. This study realizes the quantitative evaluation of county-level poverty alleviation stability by constructing the evaluation index system, taking five counties in China’s Yunnan Province as an example, using the exact 2242 survey datasets, and adopting the multiscale spatial econometric model. The main idea of the model is to obtain the score of poverty alleviation stability by weighted summing of dimensions on the basis of weight calculation of each evaluation index. Results revealed the following: (1) County-level poverty alleviation stability includes the stability of regional poverty alleviation and the stability of farmers’ poverty alleviation, which is mainly affected by the combined effect of five factors, including economic and ability status, cognitive level, supporting facilities, and social governance. (2) Based on the multiscale spatial econometric model, the overall poverty alleviation stability in the five counties is relatively low, with Zhaoyang District showing the highest stability, followed by Yiliang, Yuanyang, Honghe, and Gongshan. (3) Farmers’ poverty alleviation stability in all counties, except Zhaoyang, is higher than that in the region. County-level gross domestic product and fiscal revenue are the dominant factors affecting the stability of poverty alleviation in the region, while the dominant factors affecting the farmers’ poverty alleviation stability are the level of per capita net income and labor force proportion in the household population. (4) To enhance poverty alleviation stability, this study suggested enhancing the level of economic development in counties and strengthening the collective economy of the village, innovating the form of economic development of the village, taking the enhancement of the development capacity of counties, relying on resource advantages to actively develop special industries, and improving the stability and sustainability of income generation for farmers. Meanwhile, we propose to further improve the conditions of regional infrastructure and enhance the capacity of public services. The findings can help enrich the theoretical research system of rural economics, expand the scope of research on small-holder farming systems, and provide a reference for diversification of small farm holders economy, the improvement of agricultural farming technology, and the high-quality development of regional economy in China.