Integrated energy system(IES) is a viable route to “carbon peak and carbon neutral”. As the basis and cornerstone of economic operation and security of IES, energy flow calculation(EFC) has been widely studied. Trad...
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Integrated energy system(IES) is a viable route to “carbon peak and carbon neutral”. As the basis and cornerstone of economic operation and security of IES, energy flow calculation(EFC) has been widely studied. Traditional EFC focuses on the single or distributed slack bus models, which results in the lack of unlimited power to maintain system operation, especially for electric power grid working in islanded or coupled mode. To deal with this problem, this paper proposes a network-based virtual-slack bus(VSB) model in EFC. Firstly, considering the anticipated growth of energy conversion units(ECUs) with power adjustment capacity, the generators and ECUs are together modeled as a virtual slack bus model to reduce the concentrated power burden of IES. Based on this model, a power sensitivity method is designed to achieve the power sharing among the ECUs, where the power can be allocated adaptively based on the network conditions. Moreover, the method is helpful to maintain the voltage and pressure profile of IES. With these changes, a dynamic energy flow analysis including virtual slack bus types is extended for *** can realize the assessment of the system state. Finally, simulation studies illustrate the beneficial roles of the VSB model.
Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many proje...
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Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.
The trade-off between strength and ductility remains a persistent obstacle in the development of ad-vanced structural *** the present study,a novel dual-heterogeneous structure with a bimodal grain distribution in bot...
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The trade-off between strength and ductility remains a persistent obstacle in the development of ad-vanced structural *** the present study,a novel dual-heterogeneous structure with a bimodal grain distribution in both ferrite and austenite phases was fabricated via cold rolling and partial recrys-tallization annealing on solution-treated 2205 duplex stainless steel(DSS).The processed steel exhibited superior mechanical properties,with the yield strength increasing from 586 MPa to 903 MPa,and the ultimate tensile strength from 796 MPa to 1082 MPa,while maintaining a high total elongation of 35.3％.Based on in-situ electron backscatter diffraction(EBSD)and scanning electron microscope(SEM)analyses,the microstructural deformation behavior and strengthening mechanisms of the dual-heterostructured 2205 DSS were *** outstanding combination of strength and ductility was ascribed to the synergistic effects of grain refinement,dislocation strengthening,and hetero-deformation induced(HDI)***,the high ductility in DSS was attributed to the co-activation of cross-slip sys-tems in ferrite {110} and {112} along with the single-slip systems in austenite {111}.These findings pro-vide a new strategy for the design and development of high-strength and ultra-high-strength DSSs.
Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal...
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Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal periodicity and abnormal fluctuation characteristics of SST or the importance of historical SST data from different times;thus, these methods suffer from low prediction accuracy. To solve this problem, we comprehensively consider the effects of seasonal periodicity and abnormal fluctuation characteristics of SST data, as well as the influence of historical data in different periods, on prediction accuracy. We propose a novel ensemble learning approach that combines the Predictive Recurrent Neural Network(PredRNN) network and an attention mechanism for effective SST field prediction. In this approach, the XGBoost model is used to learn the long-period fluctuation law of SST and to extract seasonal periodic features from SST data. The exponential smoothing method is used to mitigate the impact of severely abnormal SST fluctuations and extract the a priori features of SST data. The outputs of the two aforementioned models and the original SST data are stacked and used as inputs for the next model, the PredRNN network. PredRNN is the most recently developed spatiotemporal deep learning network, which simulates both spatial and temporal representations and is capable of transferring memory across layers and time steps. Therefore, we used it to extract the spatiotemporal correlations of SST data and predict future SSTs. Finally, an attention mechanism is added to capture the importance of different historical SST data, weigh the output of each step of the PredRNN network, and improve the prediction accuracy. The experimental results on two ocean datasets confirm that the proposed approach achieves higher training efficiency and prediction accuracy than the existing SST field prediction approaches do.
Deconvoluting a defocused image with a degradation function, also named as point spread function, is an effective method for image de-blurring. However, the blurring kernel of a point spread function is highly suscept...
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Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world a...
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Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components(e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e.,HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100%at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.
Offline reinforcement learning (RL) algorithms, which rely solely on static datasets, face significant challenges: (1) Insufficient coverage of the offline dataset can lead to substantial errors when the agent selects...
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This study proposes a mobile device sensor-based emotion sensing system for hazard-involved operational action environments. In this study, the system utilizes accelerometers and gyroscopes on mobile devices for actor...
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To understand the recent advancements in the application of artificial intelligence in the field of signal processing, this paper employs VOSviewer and CiteSpace to conduct a quantitive analysis and visualization of r...
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Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can prov...
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Cloud storage is now widely used, but its reliability has always been a major concern. Cloud block storage(CBS) is a famous type of cloud storage. It has the closest architecture to the underlying storage and can provide interfaces for other types. Data modifications in CBS have potential risks such as null reference or data *** verification of these operations can improve the reliability of CBS to some extent. Although separation logic is a mainstream approach to verifying program correctness, the complex architecture of CBS creates some challenges for verifications. This paper develops a proof system based on separation logic for verifying the CBS data modifications. The proof system can represent the CBS architecture, describe the properties of the CBS system state, and specify the behavior of CBS data modifications. Using the interactive verification approach from Coq, the proof system is implemented as a verification tool. With this tool, the paper builds machine-checked proofs for the functional correctness of CBS data modifications. This work can thus analyze the reliability of cloud storage from a formal perspective.
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