Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant *** with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternati...
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Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant *** with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium *** existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless ***,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing *** overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely *** be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep ***,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication *** with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.
Few-shot learning, as an effective approach to solve image classification problems in data-scarce scenarios, has made significant progress in recent years, with numerous methods emerging. These methods typically use c...
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Few-shot learning, as an effective approach to solve image classification problems in data-scarce scenarios, has made significant progress in recent years, with numerous methods emerging. These methods typically use convolutional neural networks (CNNs) as feature extractors and classify otherdata based on the features of a small number of labeled samples. The reason CNNs have become the preferred method for image processing tasks is primarily due to their translational equivariance. However, conventional CNNs lack inherent mechanisms to handle other symmetry transformations (such as rotation andreflection), resulting in reduced classification performance of the model, especially in few-shot scenarios. To address this problem, we leverage the advantages of group convolutions in handling broader symmetric transformations, integrating them into few-shot learning tasks, and accordingly propose a group-equivariant prototypical learning network. This method maps samples into the group space via a group convolution module, enhancing the model's ability to handle various symmetry transformations present in classification targets within images, thereby improving its feature representation capability. Additionally, we designed a new contrastive loss that can naturally be co-optimized with cross-entropy loss, guiding the model to learn a highly discriminative group feature space. The experimental results on the miniImageNet, CIFAr-FS, and CUB-200 datasets show that the GEPL method significantly improves classification performance, thus verifying the effectiveness of our method.
Enterprise risk assessment not only provides a crucial reference for enterprises' strategic and business decisions, but also forms a fundamental basis for the financing decisions of banks and other financial insti...
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Enterprise risk assessment not only provides a crucial reference for enterprises' strategic and business decisions, but also forms a fundamental basis for the financing decisions of banks and other financial institutions. Furthermore, as a critical node within the industrial chain, the enterprise's risk may directly affect the stability of the entire industrial chain, highlighting the significance of researching enterprise risk assessment. Existing enterprise risk assessment methods need to be revised to account for the risk transmission between enterprises across different types of relationships. Consequently, it leads to the need for more utilization of industrial chain structure and interaction information between enterprises. To address this problem, an enterprise risk assessment model, which is based on attention mechanism and graph network, is proposed. Firstly, weights of associated enterprises under a particularrelationship are focused on. Then, weights of different relationships are introduced. After that, feature aggregation is conducted. Finally, features are put into the classification network to determine the risk category of the target enterprise, and enterprise risk assessment is accomplished. Experiments using dataset in integrated circuit industrial chain are conducted to verify this method, and the result shows that the method can effectively assess enterprise risk.
In this work, an improved wall boiling model for nanofluids was proposed, taking into account the effect of bubble slip on the wall heat flux density, as well as the effects of nanofluid thermophysical properties and ...
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In this work, an improved wall boiling model for nanofluids was proposed, taking into account the effect of bubble slip on the wall heat flux density, as well as the effects of nanofluid thermophysical properties and nanoparticle deposition on the density of nucleation sites and bubble departure diameter. The nucleation site density and bubble departure diameter were calculated and compared with the experimental data, the deviations were not more than 7.80% and 9.81%, respectively. The downward-facing surface's critical heat flux (CHF) was numerically simulated using Al2O3-H2O nanofluids. The simulation results of CHF were compared with the experimental data, and the maximum deviation did not exceed +/- 16.9%. Compared to pure water, the average CHF enhancement of 0.001-0.01vol% Al2O3-H2O nanofluid was 65.4%. The impacts of thermophysical properties, contact angle, and surface roughness were analyzed separately using the control variable method. The results showed that the wall temperature and void fraction were mostly not affected by thermophysical properties, and the variation in contact angle and surface roughness had a substantial impact. For the CHF improvement of 0.001vol% Al2O3-H2O nanofluids, the proportions of contact angle, surface roughness, and thermophysical properties on the CHF enhancement are 57%, 8%, and 1%. CHF enhancement with nanofluid was strongly related to the changes in contact angle and surface roughness.
diamond turning based on tool servos is gaining applications increasingly in the surface forming of complex-shaped optics. However, current fast/slow tool servo techniques encounter inherent limitations in terms of st...
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diamond turning based on tool servos is gaining applications increasingly in the surface forming of complex-shaped optics. However, current fast/slow tool servo techniques encounter inherent limitations in terms of stroke or bandwidth, posing a significant challenge for high-precision and high-efficiency machining of structures with relatively large strokes. To handle this dilemma, this study proposes a master-slave coordinated control strategy for fast and slow cooperative tool servo diamond turning. Specifically, the slow tool servo (STS) serves as the master servo to follow the desired toolpath, while an additional fast tool servo (FTS) axis acts as the slave servo to track the motion error of the master servo in real time. To enhance the tracking performance of the slave servo, a frequency response data-based enhancedreal-time iterative compensation method is developed to control the fast axis. As a validation, the ultraprecision diamond turning experiments are conducted on a three-axis lathe equipped with a custom-designed FTS. The servo motion is performed with high accuracy and a microlens array is generated with high quality, suggesting that the proposed strategy effectively eliminates the motion errors of the STS and significantly improves the form accuracy of the fabricated surfaces.
Phenol formaldehyde resin (PFr) has been widely used in many fields, including adhesion, refractory, coating, mold, and foam plastics. However, with the depletion of fossil fuels and more concerns on carbon neutral in...
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Phenol formaldehyde resin (PFr) has been widely used in many fields, including adhesion, refractory, coating, mold, and foam plastics. However, with the depletion of fossil fuels and more concerns on carbon neutral in recent years, the application of bio-oil to prepare bio-oil formaldehyde resin (BFr) forreplacing phenol with bio-oil is arousing great interest, which is beneficial forreducing the carbon emission and the production cost. However, the thermal stability of BFr is far from satisfactory. More flammable and toxic volatile substances will be released from BFr than from PFr, which strongly poses safety and healthy risks to the fabricating workers and the users of the resin. In this work, the thermal stability of BFr is investigated by TG and Py-GC/MS methods, for better understanding the reasons behind the low thermal stability of BFr and for improving the thermal stability of the resin. The content of phenols and the presence of some non-phenolic compounds in bio-oil, as well as the catalyst dosage of NaOH, are studied. It is found that acetic acid has a strong inhibiting role to the reaction. The addition of NaOH can improve the thermal stability, but a large amount of NaOH may reversely promote the release of volatile matters. The separation of acetic acid before polymerization is a better choice than the addition of more NaOH. The reaction is found more complex beyond the factors discussed, so more parameters such as the curing conditions are necessary for further investigations in future work.
Predicting reliability is an essential part of the design phase for ensuring mission success. In the realm of gravitational wave detection, the integrity anddependability of the Grabbing, Positioning, andrelease Mec...
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Predicting reliability is an essential part of the design phase for ensuring mission success. In the realm of gravitational wave detection, the integrity anddependability of the Grabbing, Positioning, andrelease Mechanism (GPrM) are paramount for its effective space-baseddeployment. However, predicting reliability of a complex mechanical system like the GPrM during the design phase poses significant challenges due to the harsh and variable conditions of the space environment. This paper addresses these challenges by proposing a reliability evaluation method that combines the stochastic anddegradation characteristics of the GPrM, according to which both randomness of the stud position and the degradation of the piezoelectric coefficient are considered. Failure criteria are established based on mission performance indicators, and the Kaplan-Meier estimator is used to analyze the mechanism reliability of the GPrM over time. The result predicts that the reliability of the GPrM is around 0.8 after 107 operating cycles. This finding demonstrates that our proposed method is an effective approach for evaluating reliability, offering valuable insights for ensuring the long-term performance and successful operation of precision mechanisms.
The dynamic shear modulus G and its degradation tendency G/Gmax are essential parameters for characterizing the dynamic properties of soil. Coastal facilities constructed on calcareous sand foundations are subjected t...
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The dynamic shear modulus G and its degradation tendency G/Gmax are essential parameters for characterizing the dynamic properties of soil. Coastal facilities constructed on calcareous sand foundations are subjected to multiple types of cyclic loadings. To investigate the effects of the uniformity coefficient Cu, effective confining pressure sigma 0 ', relative density dr, and median grain size d50 on the dynamic G and its degradation rule for calcareous sand, a set of resonant column tests is conducted on calcareous sand with different grain-size-distribution curves. The experimental results clearly show that the G degrades faster for calcareous sand with a lower sigma 0 ' and a larger Cu. By contrast, the degradation of the G is affected less by dr andd50. The G/Gmax values decreased slightly as gamma increased but remained less than 10-5, whereas the G/Gmax curve descendedrapidly as gamma increased beyond 10-5. Based on the test results obtained in this study and previously publisheddata, a new mathematical model characterizing the degradation tendency of G/Gmax with respect to the shear strain level of calcareous sands with different gradation conditions is proposed. The relevant parameters associated with the proposed model are correlated with Cu and sigma 0 '.
Average consensus is a cornerstone of distributed systems, facilitating essential functionalities such as distributed information fusion, decision-making, anddecentralized control. Achieving average consensus typical...
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Average consensus is a cornerstone of distributed systems, facilitating essential functionalities such as distributed information fusion, decision-making, anddecentralized control. Achieving average consensus typically relies on explicit exchanges of state and identity information among neighboring nodes. This reliance poses a risk of exposing sensitive information, leading to unpredictable data leakage. Therefore, it is imperative to implement privacy-preserving mechanisms within average consensus protocols. However, existing privacy-preserving approaches primarily focus on protecting the initial states of all nodes, compromising the identity information of the nodes. This vulnerability can be particularly problematic in scenarios where preserving identity information is paramount. In this paper, we propose a novel pulse-coupled oscillator-based privacy-preserving average consensus algorithm. Unlike traditional methods that exchange explicit state information through data packet transmission, our approach utilizes simple, identical, and content-free pulses. This method not only enhances the preservation of identity information but is also well-suited for hostile communication environments, such as during jamming attacks that disrupt data packet transmission. Furthermore, our algorithm does not necessitate synchronized clocks among all nodes, enhancing its suitability for practical applications. Numerical simulations are presented to validate the effectiveness of our theoretical results.
The Wumeng Mountain area in china is highly susceptible to landslides due to tectonic movements, heavy monsoon rains, and human activities. despite their small size, these landslides can cause significant damage. The ...
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The Wumeng Mountain area in china is highly susceptible to landslides due to tectonic movements, heavy monsoon rains, and human activities. despite their small size, these landslides can cause significant damage. The 2024 Liangshui landslide, with a volume of about 70,000 m3, resulted in 44 fatalities and marked the third major winter geological disaster in the Zhenxiong range in recent years. Comprehensive research on this landslide is crucial for understanding its triggers and failure mechanisms. Using the rapid Mass Movement Simulation dynamic model andremote sensing data, we analyzed the dynamic evolution of the Liangshui landslide, following a thorough survey of its characteristics and potential failure mechanisms. Our findings indicate that landslide-prone strata, fracturedrock, and steep source areas provided the necessary material and mechanical foundation. Seepage of fracture water after a prolonged precipitation softened the rock mass anddecreased slope stability. A sudden drop in temperature and snowfall caused frost heave, leading to the expansion of rock fractures and ultimately triggering the landslide. This research provides insight into how small landslides can induce majordisasters in the Wumeng Mountain area, as well as those triggered by prolongedrainy (or snowy) conditions in Zhenxiong County.
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