The increasing popularity of electric vehicles (EVs) has led to a growing need for electrified transportation, which has further deepened the interconnectedness of power and transportation networks. To account for EV drivers' preferences in route selection, this paper proposes an environment-aware dispatch model that coordinates the integrated power and transportation network to achieve greater economic benefits and increased utilization of renewable energy. The model employs discrete choice models with an environmental factor to represent user behavior. The constructed user behavior models serve as the basis for the integrated optimal traffic-power flow (IOTPF) model, which aims to optimize the flow of both power and traffic while minimizing the total cost of EV travel and maximizing the use of renewable energy sources. The paper employs a decentralized optimization algorithm to solve the equilibrium flows of the IOTPF model. Numerical results illustrate how the environment-aware user behavior impacts travel costs and the use of renewable energy, demonstrating the effectiveness of the IOTPF model in coordinating the optimal strategy of the integrated power-transportation network.
Breast cancer is a major health concern affecting women worldwide. Early detection and accurate diagnosis of breast cancer are crucial for improving patient outcomes. In recent years, deep learning techniques have been increasingly applied to medical imaging, including mammography, for the detection and diagnosis of breast cancer. In this study, we proposed a deep learning-based approach using the EfficientNet architecture for the detection and classification of breast cancer. We evaluated the performance of our proposed approach using mammography images from the CBIS-DDSM dataset and achieved accuracy of 0.75 and AUC of 0.83. Our results demonstrate the effectiveness of using deep learning techniques in medical imaging for breast cancer detection and diagnosis.
Cyberspace mimic defense (CMD) is an emerging defense technology that combines dynamic heterogeneous redundancy (DHR) to enhance the security of information systems. At present, the applications of CMD focus more on data tamper resistance and do not more pay attention to the privacy disclosure of executors. This paper proposes to introduce the secret sharing mechanism into the CMD and use a secret sharing scheme with dimension switching to achieve the security of the private data of executors without adding extra key management. And experiments proved that the combination of the secret sharing scheme and mimic defense can increase data security at a tolerable cost.
This paper is concerned with a search-number-reduced guessing random additive noise decoding (GRAND) algorithm for linear block codes, called partially constrained GRAND (PC-GRAND). In contrast to the original GRAND, which guesses error patterns without constraints, the PC-GRAND guesses only those error patterns satisfying partial constraints of the codes. In particular, the PC-GRAND takes partial rows of the parity-check matrix as constraints for generating candidate error patterns and the remaining rows as checks for validating the candidates. The number of searches can be reduced when the serial list Viterbi algorithm (SLVA) is implemented for searching over a trellis specified by the partial parity-check matrix. This is confirmed by numerical results. Numerical simulations are also provided for comparison with other decoding algorithms. The parallel implementation of PC-GRAND is also presented in this paper.
Most existing methods for few-shot image classification treat the support features and query features separately, and the category of a query image is determined based on its similarity to the support images. However, the relationships between the query features and support features are often ignored. In this paper, we propose a strategy that exploits the relationships between query and support features and give more weights to the highly related features. A cross-feature fusion method is further proposed to combine with metric learning to reduce overfitting. Extensive experiments on a wide range of datasets show that our method has achieved advanced results.
