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As the scale of units within virtual power plants (VPPs) continues to expand, establishing an effective operational game model for these internal units has become a pressing issue for enhancing management and operations. This paper integrates photovoltaic generation, wind power, energy storage, and constant-temperature responsive loads, and it also considers micro gas turbines as auxiliary units, collectively forming a typical VPP case study. An operational optimization model was developed for the VPP control center and the micro gas turbines, and the game relationship between them was analyzed. A Stackelberg game model between the VPP control center and the micro gas turbines was proposed. Lastly, an improved D3QN (Dueling Double Deep Q-network) algorithm was employed to compute the VPP's optimal operational strategy based on Stackelberg game theory. The results demonstrate that the proposed model can balance the energy complementarity between the VPP control center and the micro gas turbines, thereby enhancing the overall economic efficiency of operations. [ABSTRACT FROM AUTHOR]

In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature enhancement subnetwork (FEnet) and an original resolution subnetwork (ORSnet). FEnet extracts multi-resolution information and utilizes an adaptive feature selection module to enhance global features in different dimensions. The enhanced features are then fed into ORSnet as complementary features, which extract local enhancement features at the original image scale to achieve semantically consistent and visually superior enhancement effects. Experimental results on the UIEB dataset demonstrate that our method achieves the best performance compared to the state-of-the-art methods. Furthermore, through comprehensive application testing, we have validated the superiority of our proposed method in feature extraction and enhancement compared to other end-to-end underwater image enhancement methods. [ABSTRACT FROM AUTHOR]

Point clouds represent an important way for robots to perceive their environments, and can be acquired by mobile robots with LiDAR sensors or underwater robots with sonar sensors. Hence, real-time semantic segmentation of point clouds with onboard edge devices is essential for robots to apprehend their surroundings. In this paper, we propose an onboard point cloud semantic segmentation system for robotic platforms to overcome the conflict between attaining high accuracy of segmentation results and the limited available computational resources of onboard devices. Our system takes raw a sequence of point clouds as inputs, and outputs semantic segmentation results for each frame as well as a reconstructed semantic map of the environment. At the core of our system is the transformer-based hierarchical feature extraction module and fusion module. The two modules are implemented with sparse tensor technologies to speed up inference. The predictions are accumulated according to Bayes rules to generate a global semantic map. Experimental results on the SemanticKITTI dataset show that our system achieves +2.2% mIoU and 18× speed improvements compared with SOTA methods. Our system is able to process 2.2 M points per second on Jetson AGX Xavier (NVIDIA, Santa Clara, USA), demonstrating its applicability to various robotic platforms. [ABSTRACT FROM AUTHOR]

A 3D point cloud is one of the main data sources for robot environmental cognition and understanding. Due to the limited computation and memory capacities of the robotic platform, existing semantic segmentation models of 3D point clouds cannot meet the requirements of real-time applications. To solve this problem, a lightweight, fully convolutional network based on an attention mechanism and a sparse tensor is proposed to better balance the accuracy and real-time performance of point cloud semantic segmentation. On the basis of the 3D-Unet structure, a global feature-learning module and a multi-scale feature fusion module are designed. The former improves the ability of features to describe important areas by learning the importance of spatial neighborhoods. The latter realizes the fusion of multi-scale semantic information and suppresses useless information through the task correlation learning of multi-scale features. Additionally, to efficiently process the large-scale point clouds acquired in real time, a sparse tensor-based implementation method is introduced. It is able to reduce unnecessary computation according to the sparsity of the 3D point cloud. As demonstrated by the results of experiments conducted with the SemanticKITTI and NuScenes datasets, our model improves the mIoU metric by 6.4% and 5%, respectively, over existing models that can be applied in real time. Our model is a lightweight model that can meet the requirements of real-time applications. [ABSTRACT FROM AUTHOR]

In recent years, the volume of spatial data has rapidly grown, so it is crucial to process them in an efficient manner. The level of parallel processing in big data platforms such as Hadoop and Spark is determined by partitioning the dataset. A common approach is to split the data into chunks based on the number of bytes. While this approach works well for text-based batch processing, in many cases, it is preferable to take advantage of the structured information contained in the dataset (e.g., spatial coordinates) to plan data partitioning. In view of the huge amount of data and the impossibility of quickly establishing partitions, this paper designs a method for approximate partition boundary solving, which divides the data space into multiple non-overlapping symmetric bins and samples each bin, making the probability density of the sampling set bounded by the deviation of the probability density of the original data. The sampling set is read into the memory at one time for calculation, and the established partition boundary satisfies the partition threshold-setting. Only a few boundary adjustment operations are required, which greatly shortens the partition time. In this paper, the method proposed in the paper is tested on the synthetic dataset, the bus trajectory dataset, and six common spatial partitioning methods (Grid, Z-curve, H-curve, STR, Kd-tree, and R*-Grove) are selected for comparison. The results show that the symmetric bin sampling method can describe the spatial data distribution well and can be directly used for partition boundary division. [ABSTRACT FROM AUTHOR]

Hydrogen energy is a clean, zero-carbon, long-term storage, flexible and efficient secondary energy. Accelerating the development of the hydrogen energy industry is a strategic choice to cope with global climate change, achieve the goal of carbon neutrality, and realize high-quality economic and social development. This study aimed to analyze the economic impact of introducing value-added services to the hydrogen energy market on hydrogen energy suppliers. Considering the network effect of value-added services, this study used a two-stage game model to quantitatively analyze the revenue of hydrogen energy suppliers under different scenarios and provided the optimal decision. The results revealed that (1) the revenue of a hydrogen energy supplier increases only if the intrinsic value of value-added services exceeds a certain threshold; (2) the revenue of hydrogen energy suppliers is influenced by a combination of four key factors: the intrinsic value of value-added services, network effects, user scale, and the sales strategies of rivals; (3) the model developed in this paper can provide optimal decisions for hydrogen energy suppliers to improve their economic efficiency and bring more economic investment to hydrogen energy market in the future. [ABSTRACT FROM AUTHOR]

China's government launched a policy in October 2017 to permit the distributed generators to peer-to-peer trade their electricity generation on the market. Several clauses in the policy document are, however, unclear and ambiguous. This work identifies three vital but not clearly detailed issues in the policy document: (1) participation eligibility, (2) the grid fee calculation method, and (3) subsidy rates. Then, we carry out a comprehensive analysis of the economic impacts of the trade policy based on a case study of an eastern city in China. Sensitivity analyses on the impacts of the subsidy rates, transmission and distribution prices (TDPs), and end-user regulated prices are conducted. The results show that the trading policy will benefit the photovoltaic (PV) generators with more revenue by 6–11%, reduce the cost for end-users by 6–12%, and decrease the revenue of the power grid company by 32–55%. [ABSTRACT FROM AUTHOR]

As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maximum utility and meet the global QoS constraints with low time cost. In this paper, we propose a dynamic service selection approach based on adaptive global QoS constraints decomposition. Fuzzy logic technology and Cultural Genetic Algorithm are used to adaptively decompose global QoS constraints into near-optimal local constraints. According to the near-optimal local constraints, the optimal service is selected for each service class during the running time efficiently. Experimental results show that the proposed approach not only achieves the near-optimal solution, but also significantly reduces the computation time, and has good adaptability and scalability. [ABSTRACT FROM AUTHOR]

With economic globalization, the supply-and-demand gap of China's minerals is becoming increasingly sharp, and the degree of dependence on imports is climbing, which poses a severe threat to the resource security for the country. From the perspectives of system and sustainable development, this paper develops a conceptual framework of mineral security, which is composed of five dimensions: availability, accessibility, technology and efficiency, sociability and governance, and environmental sustainability. Based on this framework, it constructs the evaluating metrics for measuring mineral security. Moreover, it employs the hybrid multiple criteria decision-making methods of Fuzzy analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) to assess the security performance for China's several critical minerals, namely iron, copper, aluminum, lead, zinc, and nickel, with respect to the period of 2001 to 2015. The result indicates that the critical minerals of China were at a low to moderate level of security. Iron, copper, and nickel were in an unsecure situation for their short supply in China, and showed a downswing trend. On the other hand, as the preponderant minerals, lead and zinc were at a relatively secure position and uprising; however, they were exhausting their superiority for the huge and rapid-growth economic demand. Aluminum, as a mineral that China seriously depends on for imports, also demonstrated an upward trend due to the successful management of diversity of importing sources. [ABSTRACT FROM AUTHOR]

In order to meet the real-time public travel demands, the bus operators need to adjust the timetables in time. Therefore, it is necessary to predict the variations of the short-term passenger flow. Under the help of the advanced public transportation systems, a large amount of real-time data about passenger flow is collected from the automatic passenger counters, automatic fare collection systems, etc. Using these data, different kinds of methods are proposed to predict future variations of the short-term bus passenger flow. Based on the properties and background knowledge, these methods are classified into three categories: linear, nonlinear and combined methods. Their performances are evaluated in detail in the major aspects of the prediction accuracy, the complexity of training data structure and modeling process. For comparison, some long-term prediction methods are also analyzed simply. At last, it points that, with the help of automatic technology, a large amount of data about passenger flow will be collected, and using the big data technology to speed up the data preprocessing and modeling process may be one of the directions worthy of study in the future. [ABSTRACT FROM AUTHOR]

China's rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO 2 ) pollution, which has led to greater environmental pressures. In order to mitigate the environmental pressures caused by NO 2 pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO 2 pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO 2 pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO 2 pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO 2 pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO 2 pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO 2 pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.