Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.
With the development of cloud storage technology, data storage security has become increasingly serious. Aiming at the problem that existing attributebased encryption schemes do not consider hierarchical authorities and the weight of attribute. A hierarchical authority based weighted attribute encryption scheme is proposed. This scheme will introduce hierarchical authorities and the weight of attribute into the encryption scheme, so that the authorities have a hierarchical relationship and different attributes have different importance. At the same time, the introduction of the concept of weight makes this scheme more flexible in the cloud storage environment and enables fine-grained access control. In addition, this scheme implements an online/offline encryption mechanism to improve the security of stored data. Security proof and performance analysis show that the scheme is safe and effective, and it can resist collusion attacks by many malicious users and authorization centers. It is more suitable for cloud storage environments than other schemes.
Aimed at the disadvantage of state of charge (SOC) estimation by using traditional feed forward neural network, a new method proposed to soleve the problem. The time series neural network is introduced to estimate the SOC. The experiments results show that the time series neural can estimate the SOC more accurate. In addition, the different structures and the key parameter are discussed to achieve the best performance.
<abstract><p>Cognitive green computing (CGC) is widely used in the Internet of Things (IoT) for the smart city. As the power system of the smart city, the smart grid has benefited from CGC, which can achieve the dynamic regulation of the electric energy and resource integration optimization. However, it is still challenging for improving the identification accuracy and the performance of the load model in the smart grid. In this paper, we present a novel algorithm framework based on reinforcement learning (RL) to improve the performance of non-invasive load monitoring and identification (NILMI). In this model, a knowledge base of load power facilities (LPF-KB) architecture is designed to facilitate the load data-shared collection and storage; utilizing deep convolutional neural networks (DNNs) structure based on the attentional mechanism to enhance the representations learning of load features; using RL-based Monte-Carlo tree search (MCTS) method to construct an optimal strategy network, and to realize the online combined load prediction without relying on the prior knowledge. We use the massive experiment on the real-world datasets of household appliances to evaluate the performance of our method. The experimental results show that our approach has remarkable performance in reducing the load online identification error rate. Our model is a generic model, and it can be widely used in practical load monitoring identification and the power prediction system.</p></abstract>
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