The diagnosis of high myopia using fundus images is essential for visual health. Existing deep learning-based methods rely on large-scale labeled data, but annotated data for high myopia fundus images remains scarce. To address this issue, we mimic the ability of ophthalmologists to diagnose a new disease with only a small number of samples. In this regard, we propose a meta-learning network guided by domain knowledge from fundus images for diagnosing high myopia. The model consists of three modules: First, the image reconstruction module builds an autoencoder (comprising an encoder and a decoder) that takes the input raw image and outputs the reconstructed image. Next, the fundus image domain knowledge learning module constructs a Siamese network to learn the similarity between the original and reconstructed fundus images. This similarity is used as a loss function to guide the encoder in effectively learning the domain features of fundus images. Finally, in the domain-knowledge-guided meta-learning module, the encoder’s initialization parameters (obtained from the first two modules) are further optimized using the OCMAMAL architecture, resulting in more optimal encoder parameters. This optimization helps achieve superior recognition performance with only a small amount of data for new tasks. Using a clinically real high myopia fundus image dataset, our method achieved F1 scores of 83.4%, 87.9%, and 89.2% under 5-shot, 10-shot, and 20-shot conditions, respectively, demonstrating the effectiveness of the proposed method.

Computerized tomography (CT) provides non-invasive visualization of internal structural information without losing any detail. It has proven to be very useful in protecting cultural relics. However, metal cultural relics are frequently accompanied by destructive metal artifacts in x-ray CT images, making it impossible for traditional methods to obtain detailed information from the cultural relics. In recent years, deep generative-based models have demonstrated great promise for solving such problems. However, due to the complicated structure and diverse materials of cultural relics, it is difficult to accurately restore the highly heterogeneous details of cultural relics in practical applications. As a result, the primary focus of this study is on the removal of metal items from cultural relics using deep generative models and achieves effective restoration of highly heterogeneous details by combining mask-guided strategies. Specifically, we first collaborated with the Palace Museum to build a cultural relic CT dataset and specifically divided artifacts into two categories: sharp edge smoothing and edge distortion according to their complexity. Second, many deep generative networks include CycleGAN, CSGAN, MUNIT, DeblurGAN, and DRIT were trained. Additionally, segmentation masks were blended to create artifact-free images. Finally, the performance was enhanced via dynamic weight adjustment. The effectiveness has been qualitatively and quantitatively validated on the cultural relic CT dataset. PSNR and SSIM metrics confirm the model’s ability to restore fine details, providing reliable support for future cultural relic protection.

The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems (CPSs). The performance of CPSs is closely linked to the last-mile wireless communication networks, which often become bottlenecks due to their inherent limited resources. Current CPS operations often treat wireless communication networks as unpredictable and uncontrollable variables, ignoring the potential adaptability of wireless networks, which results in inefficient and overly conservative CPS operations. Meanwhile, current wireless communications often focus more on throughput and other transmission-related metrics instead of CPS goals. In this study, we introduce the framework of goal-oriented wireless communication resource allocations, accounting for the semantics and significance of data for CPS operation goals. This guarantees optimal CPS performance from a cybernetic standpoint. We formulate a bandwidth allocation problem aimed at maximizing the information utility gain of transmitted data brought to CPS operation goals. Since the goal-oriented bandwidth allocation problem is a large-scale combinational problem, we propose a divide-and-conquer and greedy solution algorithm. The information utility gain is first approximately decomposed into marginal utility information gains and computed in a parallel manner. Subsequently, the bandwidth allocation problem is reformulated as a knapsack problem, which can be further solved greedily with a guaranteed sub-optimality gap. We further demonstrate how our proposed goal-oriented bandwidth allocation algorithm can be applied in four potential CPS applications, including data-driven decision-making, edge learning, federated learning, and distributed optimization. Through simulations, we confirm the effectiveness of our proposed goal-oriented bandwidth allocation framework in meeting CPS goals.

Sizeable lithium-ion battery (LIB) sources in the transportation and power sectors provide a promising approach to alleviate the increasing volatility in energy systems. To dispatch LIBs durably and safely, operators need to estimate the battery power characteristics, which are commonly derived from external states of the battery obtained by empirical models. However, the internal states that play a decisive role are rarely considered. In this work, the power characterization is based on an interpretable and analytical electrochemical model. In addition to external states, internal states, including Li-ion concentrations, side reaction rates, and the energy conversion efficiency, are considered in the characterization. Since the dispatch time interval is usually longer than the time resolution of the battery model, an optimization-based approach taking the idea of model predictive control is designed for efficient calculation. A linearization scheme is proposed to embed power characteristics into the optimization-based dispatch of an integrated energy-transportation system with low complexity. Case studies on LiNCM and LiFePO$_{4}$batteries in different temperatures are conducted. The calculation of power characteristics takes about two minutes. By considering power characteristics, the energy conversion efficiency of the dispatched battery can be increased by 5%–15%. At the same time, the degradation stress and heat generation can be reduced to around one-fourth of the naïve case.

Vehicle scale estimation with a single camera is a typical application for intelligent transportation and it faces the challenges from visual computing while intensity-based method and descriptor-based method should be balanced. This paper proposed a vehicle scale estimation method based on salient object detection to resolve this problem. The regularized intensity matching method is proposed in Lie Algebra to achieve robust and accurate scale estimation, and descriptor matching and intensity matching are combined to minimize the proposed loss function. The visual attention mechanism is designed to select image patches with texture and remove the occluded image patches. Then the weights are assigned to pixels from the selected image patches which alleviates the influence of noise-corrupted pixels. The experiments show that the proposed method significantly outperforms state-of-the-art methods with regard to the robustness and accuracy of vehicle scale estimation.

The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information, which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the maximum information coefficient (MIC), which can find the correlations between the nontechnical loss and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.