Remote sensing target detection is a critical technology in fields such as intelligent transportation, resource exploration, and environmental protection. However, challenges in computer vision arise due to issues in remote sensing imagery, including relatively weak feature extraction capabilities, insufficient feature representation, high difficulty in target localization, and large model parameter counts. To address these challenges, this paper proposes a Spatial-Frequence Semantic-Detail network (SSD-Net) for remote sensing object detection. SSD-Net comprises two plug-and-play modules (SFD and MMFE) and a feature fusion framework (SDFF). These components collectively reduce feature loss during extraction, enhance local perception and localization capabilities, suppress background noise interference, strengthen shape perception of remote sensing targets, and improve the correlation between semantic and feature information, thereby enabling target detection against complex backgrounds. We validated the model’s effectiveness on three public remote sensing datasets (DIOR, TGRS-HRRSD, and RSOD), and achieving accuracies of 88.9%, 97.1%, and 99.3% (in terms of mAP50), respectively. These results surpass several benchmark models and state-of-the-art (SOTA) approaches.

In this article, a multiobjective optimization methodology is tailored for energy producers and sellers (Prosumers) and electric vehicle charging service providers (EVCS) in IES. The primary objective is to harmonize economic and safety aspects of system, while addressing uncertainties associated with renewable energy and involvement of electric vehicles (EVs) in integrated demand response (IDR). Initially, an orderly charging model for EVs is developed by driving behavior patterns, taking into account the influence of EVCS aggregating EVs in IDR via photovoltaic-energy storage charging stations. Subsequently, a stochastic programming method with conditional value at risk is utilized to alleviate potential risks from stochastic and intermittent nature of renewable energy outputs. Furthermore, the optimization framework incorporates operating costs of Prosumers and EVCS, and peak-to-valley difference of IES load as multiobjective functions. The optimal solution is determined by technique for order of preference by similarity to ideal solution. Ultimately, case studies confirm that the proposed approach effectively balances economic performance for multiple stakeholders and operational safety, and enhances system robustness under uncertain conditions.

ABSTRACT Malignant lung nodules can significantly affect patients' normal lives and, in severe cases, threaten their survival. Owing to the heterogeneity of computed tomography scans and the varying sizes of nodules, physicians often face challenges in diagnosing this condition. Therefore, a novel adaptive multi‐channel fusion network (AMCF‐Net) is proposed for computer‐aided diagnosis of lung nodules. First, a Multi‐Channel Fusion Model module is designed, which divides the channels into two parts in specific proportions, effectively extracting multi‐scale channel information while reducing network parameters. After the feature maps output at each layer of the AMCF‐Net, a novel adaptive depth‐wise separable convolution with a squeeze‐and‐excitation module is designed to adaptively integrate the feature maps of various stages of the AMCF‐Net, ensuring that the key lesions of lung nodules are not lost during classification. Finally, a hybrid loss scheme based on an adaptive mixing ratio is proposed to solve the problem of an imbalanced number of positive and negative nodule samples in the dataset. The model achieved the following test results: an accuracy of 90.22%, a specificity of 98.19%, an F1‐score of 86.57%, a sensitivity of 86.49%, and a G‐mean of 87.72%. Compared with other advanced networks, AMCF‐net delivers high‐precision lung nodule classification with minimal inference cost. Related codes have been released at: https://github.com/GuYuIMUST/AMCF‐net .

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.

In the above article[1], there was a typographical error in Fig. 2. The correct figure appears below: