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The detection of abnormal targets in transmission lines plays a significant role in maintaining the stability and safety of transmission systems. To achieve improved detection performance for abnormal targets, we propose a new target detector based on YOLOX, called YOLOX++. First, a multiscale cross-stage partial network (MS-CSPNet) is designed, which fuses multiscale feature information and expands the receptive field of the target through channel combination. Furthermore, depthwise and dilated convolutions are introduced in an object decoupling head to better capture the long-range dependencies of objects in feature maps. Finally, the alpha loss function ( $\alpha $ -IoU) is introduced to optimize the localization of small objects. Experiments show that in a comparison with the YOLOX model, the YOLOX++ approach in this paper achieves 86.8% and 96.6% detection accuracies for high-voltage tower bird nest and power line insulator targets, respectively. On the PASCAL VOC dataset, the AP50 and APS are improved by 9.3% and 5.0% over those of YOLOX, respectively, showing that the YOLOX++ network possesses better robustness for small target detection.

Knowledge base completion (KBC) aims to predict missing information in a knowledge base. Most existing embedding-based KBC models assume that all test entities are available at training time. Thus, a question arises-that is, how to answer queries concerning test entities not observed at training time, which is called the out-of-knowledge-base (OOKB) entity problem. In this article, we propose a parameter-efficient embedding model that combines the benefits of a graph neural network (GNN) and a convolutional neural network (CNN) to solve the KBC task with OOKB entities. First, we apply the GNN architecture to learn the information between nodes in the graph. Second, convolution layers are used as a transition matrix in GNN to learn more expressive embeddings with fewer parameters. Finally, we use a transition-based knowledge graph embedding model to solve the KBC task. The model has learnable weights that adapt based on information from neighbors and can exploit auxiliary knowledge for OOKB entities to compute their embedding while remaining parameter efficient. We demonstrate the effectiveness of the proposed model on OOKB datasets, and the code is available at https://github.com/Tianchen627/Knowledge-Transfer-for-Out-of-Knowledge-Base-Entities.

With the distributed sensors that are deployed to monitor the urban environment in smart cities, the sensed data are overwhelming and beyond the scope of the wireless sensor networks (WSNs) capability. Due to the communication range limits of the sensors, most of these data will be outsourced and stored on some untrusted servers. Thus, how to maintain the data confidentiality and integrity, as well as source authentication and data query privacy of the outsourced data, is a challenging problem. In this paper, we propose a secure searchable encryption scheme, named SSE, for urban sensing and querying to address the problem. Specifically, our SSE constructs a secure hidden vector encryption-(HVE-) based rang query predicate. The sensed data can be stored on an untrusted server in encrypted form. A requester can obtain the correct ciphertexts when his authorized range query matches the HVE-based encryption predicate. With the help of the base station, the ciphertexts can be decrypted and data integrity can be verified; then, the requester can obtain the correct original data. Security analysis demonstrates that; in the SSE, only the authorized requesters can obtain the query results, while the data confidentiality and integrity and source authentication are also preserved.

False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively bypass the detection model to threaten the power grid system. A generative adversarial network (GAN) framework is first constructed by combining residual networks (ResNet) with fully connected networks (FCN). Then, a sparse adversarial learning model is built by integrating the time-aligned data and normal data, which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation. Furthermore, we introduce a Gaussian hybrid distribution matrix by aggregating the network structure of attack data characteristics and normal data characteristics, which can connect and calculate FDIA data with normal characteristics. Finally, efficient FDIA attack samples can be sequentially generated through interactive adversarial learning. Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data, and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength, robustness, and covert capability. [ABSTRACT FROM AUTHOR]