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Rational planning and the use of renewable energy are effective means to reduce urban carbon emissions. In view of the few urban renewable energy planning cases and unclear methods, the paper takes the Sino-Singapore Tianjin Eco-City renewable energy planning project as a case to provide a renewable energy planning method under the guidance of carbon constraints. Based on scenario analysis, the energy demand of urban buildings, industry and transportation is analyzed and predicted. On the basis of meeting the needs of terminal energy use, with the goal of reducing carbon emissions, the renewable energy planning scheme from 2021 to 2050 under the low-carbon scenario has been formulated, including the promotion of energy-efficient buildings, the utilization of renewable energy in buildings, the electrification of terminal energy use, and the application of large-scale municipal renewable energy. It is planned that, by 2050, the overall renewable energy utilization rate of the Sino-Singapore Tianjin Eco-City will reach 76.76%. It will use renewable energy to heat about 60 million square meters, generate about 766 million kWh of electricity, save about 0.723 million tons of standard coal and reduce 1.287 million tons of carbon dioxide every year, which will have a good effect of energy conservation and emission reduction. In this paper, the renewable energy planning method under the guidance of carbon constraint is established, which can achieve the purposes of saving resources, protecting the environment and driving sustainable development. The Sino-Singapore Tianjin Eco-City is an international co-creation city, which will receive extensive attention and provide theoretical guidance and demonstration cases for urban renewable energy planning in the context of carbon peak and carbon neutrality in the new era. [ABSTRACT FROM AUTHOR]

X-ray security inspection processes have a low degree of automation, long detection times, and are subject to misjudgment due to occlusion. To address these problems, this paper proposes a multi-objective intelligent recognition method for X-ray images based on the YOLO deep learning network and an optimized transformer structure (YOLO-T). We also construct the GDXray-Expanded X-ray detection dataset, which contains multiple types of dangerous goods. Using this dataset, we evaluated several versions of the YOLO deep learning network model and compared the results to those of the proposed YOLO-T model. The proposed YOLO-T method demonstrated higher accuracy for multitarget and hidden-target detection tasks. On the GDXray-Expanded dataset, the maximum mAP of the proposed YOLO-T model was 97.73%, which is 7.66%, 16.47%, and 7.11% higher than that obtained by the YOLO v2, YOLO v3, and YOLO v4 models, respectively. Thus, we believe that the proposed YOLO-T network has good application prospects in X-ray security inspection technologies. In all kinds of security detection scenarios using X-ray security detectors, the model proposed in this paper can quickly and accurately identify dangerous goods, which has broad application value. [ABSTRACT FROM AUTHOR]

The modern urban environment is becoming more and more complex. In helping us identify surrounding objects, vehicle vision sensors rely more on the semantic segmentation ability of deep learning networks. The performance of a semantic segmentation network is essential. This factor will directly affect the comprehensive level of driving assistance technology in road environment perception. However, the existing semantic segmentation network has a redundant structure, many parameters, and low operational efficiency. Therefore, to reduce the complexity of the network and reduce the number of parameters to improve the network efficiency, based on the deep learning (DL) theory, a method for efficient image semantic segmentation using Deep Convolutional Neural Network (DCNN) is deeply studied. First, the theoretical basis of the convolutional neural network (CNN) is briefly introduced, and the real-time semantic segmentation technology of urban scenes based on DCNN is recommended in detail. Second, the atrous convolution algorithm and the multi-scale parallel atrous spatial pyramid model are introduced. On the basis of this, an Efficient Symmetric Network (ESNet) of real-time semantic segmentation model for autonomous driving scenarios is proposed. The experimental results show that: (1) On the Cityscapes dataset, the ESNet structure achieves 70.7% segmentation accuracy for the 19 semantic categories set, and 87.4% for the seven large grouping categories. Compared with other algorithms, the accuracy has increased to varying degrees. (2) On the CamVid dataset, compared with segmentation networks of multiple lightweight real-time images, the parameters of the ESNet model are around 1.2 m, the highest FPS value is around 90 Hz, and the highest mIOU value is around 70%. In seven semantic categories, the segmentation accuracy of the ESNet model is the highest at around 98%. From this, we found that the ESNet significantly improves segmentation accuracy while maintaining faster forward inference speed. Overall, the research not only provides technical support for the development of real-time semantic understanding and segmentation of DCNN algorithms but also contributes to the development of artificial intelligence technology. [ABSTRACT FROM AUTHOR]

With the introduction of concepts such as ubiquitous mapping, mapping-related technologies are gradually applied in autonomous driving and target recognition. There are many problems in vision measurement and remote sensing, such as difficulty in automatic vehicle discrimination, high missing rates under multiple vehicle targets, and sensitivity to the external environment. This paper proposes an improved RES-YOLO detection algorithm to solve these problems and applies it to the automatic detection of vehicle targets. Specifically, this paper improves the detection effect of the traditional YOLO algorithm by selecting optimized feature networks and constructing adaptive loss functions. The BDD100K data set was used for training and verification. Additionally, the optimized YOLO deep learning vehicle detection model is obtained and compared with recent advanced target recognition algorithms. Experimental results show that the proposed algorithm can automatically identify multiple vehicle targets effectively and can significantly reduce missing and false rates, with the local optimal accuracy of up to 95% and the average accuracy above 86% under large data volume detection. The average accuracy of our algorithm is higher than all five other algorithms including the latest SSD and Faster-RCNN. In average accuracy, the RES-YOLO algorithm for small data volume and large data volume is 1.0% and 1.7% higher than the original YOLO. In addition, the training time is shortened by 7.3% compared with the original algorithm. The network is then tested with five types of local measured vehicle data sets and shows satisfactory recognition accuracy under different interference backgrounds. In short, the method in this paper can complete the task of vehicle target detection under different environmental interferences. [ABSTRACT FROM AUTHOR]

Let G be a graph with a minimum degree δ of at least two. The inclusion chromatic index of G, denoted by χ ⊂ ′ (G) , is the minimum number of colors needed to properly color the edges of G so that the set of colors incident with any vertex is not contained in the set of colors incident to any of its neighbors. We prove that every connected subcubic graph G with δ (G) ≥ 2 either has an inclusion chromatic index of at most six, or G is isomorphic to K ^ 2 , 3 , where its inclusion chromatic index is seven. [ABSTRACT FROM AUTHOR]

The integrity of point cloud is the basis for smoothly ensuring subsequent data processing and application. For "Smart City" and "Scan to Building Information Modeling (BIM)", complete point cloud data is essential. At present, the most commonly used methods for repairing point cloud holes are multi-source data fusion and interpolation. However, these methods either make it difficult to obtain data, or they are ineffective at repairs or labor-intensive. To solve these problems, we proposed a point cloud "fuzzy" repair algorithm based on the distribution regularity of buildings, aiming at the façade of a building in an urban scene, especially for the vehicle Lidar point cloud. First, the point cloud was rotated to be parallel to the plane XOZ, and the feature boundaries of buildings were extracted. These boundaries were further classified as horizontal or vertical. Then, the distance between boundaries was calculated according to the Euclidean distance, and the points were divided into grids based on this distance. Finally, the holes in the grid that needed to be repaired were filled from four adjacent grids by the "copy–paste" method, and the final hole repairs were realized by point cloud smoothing. The quantitative results showed that data integrity improved after the repair and conformed to the state of the building. The angle and position deviation of the repaired grid were less than 0.54° and 3.25 cm, respectively. Compared with human–computer interaction and other methods, our method required less human intervention, and it had high efficiency. This is of promotional significance for the repair and modeling of point cloud in urban buildings. [ABSTRACT FROM AUTHOR]