Double-layer Network Research Articles

A Double-Layer Coupled Network Model of Network Density Effects on Multi-Stage Innovation Efficiency Dynamics: Agent-Based Modeling Methods

This paper proposes a double-layer coupled network model to analyze the multi-stage innovation activities of online, and the model consists of two layers: the online layer, which represents the virtual interactions among innovators, and the offline layer, which represents the physical interactions among innovators. The model assumes that the innovation activities are influenced by both the online and offline network structures, as well as the coupling effect between them. And it simulates the entire innovation process including knowledge diffusion and knowledge recombination. The model also incorporates the concept of network density, which measures the degree of network connectivity and cohesion (network structure). Observing the network density influence on innovation efficiency during the innovation process is realized through setting the selection mechanism and the knowledge recombination mechanism. The coupling relationship between the two layers of network density on the three stages of innovation is further discussed under the theoretical framework of the innovation value chain. Simulation and experimental results suggest that when the offline network density is constant, a higher online network density is not always better. When the online network density is low, the sparse structure of the online network reduces innovation efficiency. When the online network density is high, the structural redundancy caused by the tight network structure prevents innovation efficiency from improving. The results of the study help enterprises to adjust and optimize the internal cooperation network structure at different stages of innovation in order to maximize its effectiveness and improve the innovation efficiency of enterprises.

Flexible Strain Sensors Based on Bionic Parallel Vein-like Structures for Human Motion Monitoring.

In recent years, strain sensors have penetrated various fields. The capability of sensors to convert physical signals into electrical signals is of great importance in healthcare. However, it is still challenging to obtain sensors with high sensitivity, large operating range and low cost. In this paper, a stretchable strain sensor made of a double-layer conductive network, including a biomimetic multilayer graphene-Ecoflex (MLG-Ecoflex) substrate and a multilayer graphene-carbon nanotube (MLG-CNT) composite up-layer was developed. The combined action of the two layers led to an excellent performance with an operating range of up to 580% as well as a high sensitivity (gauge factor (GFmax) of 1517.94). In addition, a pressure sensor was further designed using the bionic vein-like structure with a multi-layer stacking of MLG-Ecoflex/MLG-CNT/MLG-Ecoflex to obtain a relatively high deformation along the direction of thickness. The device presented a high sensing performance (up to a sensitivity of 0.344 kPa-1) capable of monitoring small movements of the human body such as vocalizations and gestures. The good performance of the sensors together with a simple fabrication procedure (flip-molding) make it of potential use for some applications, for example human health monitoring and other areas of human interaction.

Cascade failure analysis of power-information interdependent network

Modern information technology has been constantly evolving, transforming the traditional power grid into a network that couples both power and information layers. Understanding the cascade failure behavior of such power communication interdependent networks is essential for effectively controlling catastrophic network failures, preventing system collapse, and ensuring normal network operation. This research can contribute to the development of tools to predict and prevent such failures, and restore normal network functions in a timely manner. This paper focuses on the modeling method and cascading fault analysis of the power-information double-layer coupling network. We construct power information interdependent networks based on IEEE30 system and England39 system, and evaluate the cascade failure results using load distribution cascade failure model and HITS algorithm. The evaluation criteria include network efficiency, residual network size, and residual network load. By analyzing these parameters, we can gain insights into the performance of the power-information interdependent networks during cascade failures. Through simulation results, we demonstrate that the type i attack proposed in this paper renders the network structure unstable and less robust compared to the degree attack, intermediate attack, and random attack. These findings provide valuable references for developing strategies to mitigate the cascading failure of power-information interdependent networks.

Optimized aspect and self-attention aware LSTM for target-based semantic analysis (OAS-LSTM-TSA)

PurposeAspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.Design/methodology/approachThis research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.FindingsTo detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.Originality/valueThe novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Wind power forecasting based on manifold learning and a double-layer SWLSTM model

With the increase of wind power installed capacity, wind energy is playing an increasingly important role in power grids. However, this also aggravates the volatility of a power grid. Accurate wind power prediction is particularly important for grid dispatching and grid connection modes. However, the existing forecasting methods mostly consider the meteorological environmental factors and historical power data and do not consider the impact of wind turbine generator parameters. Therefore, this study proposes a double-layer Shared Weight Long Short-Term Memory Network (SWLSTM) prediction model based on manifold learning considering meteorological environment factors, wind turbine generator parameters, and historical wind power. First, 17 indicators such as meteorological environment factors and wind turbine parameters are determined as influencing factors to construct high-dimensional data sets. Second, manifold learning is used to reduce the dimension of high-dimensional data sets and determine the best dimension reduction. The data after dimensionality reduction and historical wind power are used as the input of the prediction model. Finally, considering the influence of errors on the prediction results, a double-layer SWLSTM prediction model is constructed. The double-layer model is constructed simultaneously, and the final power prediction result is obtained by adding the prediction error of the lower model and the prediction wind power of the upper model. Moreover, a wind farm in Xinjiang is used to verify the effectiveness of the proposed prediction model. The results show that the prediction strategy and the model proposed in this paper have high prediction accuracy.

Some new nonlinear wave solutions and dynamical behavior of the (3+1)-dimensional Ito equation

Some new nonlinear wave solutions of the (3+1)-dimensional Ito equation, such as three-wave type solutions, lump wave solutions, rogue wave solutions and hybrid solutions of different forms, are studied by the bilinear neural network method. Besides, three-wave solutions are investigated via the single-layer neural network ‘4-3-1’ model, and hybrid soliton solutions between lump-type and strip-like soliton in ‘4-5-1’ model are discussed. Furthermore, rogue wave solutions are studied by the double-layer neural network the ‘4-2-3-1’ and the ‘4-3-3-1’ models, and the interference phenomena between them are analyzed using visual images. We also employed the extended parameter limit method to investigate the collapse degradation behavior of both solutions. Lump solutions and Y-type semi-rational solutions are derived separately.

Continuous action iterated dilemma under double-layer network with unknown nonlinear dynamics and its convergence analysis

Continuous action iterated dilemma under double-layer network with unknown nonlinear dynamics and its convergence analysis

Adaptive walking control for quadruped robot by using oscillation patterns.

To improve the adaptability of quadruped robot in multiple scenarios, an adaptive locomotive system based on the double-layered central pattern generator (CPG) is proposed. The novel CPG network consists of double master units and subsets of slave units based on gyroscope signals including yaw and pitch angle. The response of master units provides the ability to control the 1st joins of quadruped robot, while slave units can generate the symmetry signals to control the 2nd and 3rd joints. The CPG network enables the seamless switching of locomotion gaits to stops and starts by using an ultrasonic sensor. Through adjusting the mutually dependent parameters, joints can generate the joint angles to achieve steering behavior. For adaptive movement on an irregular surface, stable ranges of the robot body yaw and pitch angles are proposed by using gyroscope signals. The experimental results verify that the quadruped robot with the proposed double-layered CPG network can perform stable trot pattern in a complex environment.

DLAReID: double-layer attention network for object re-identification

DLAReID: double-layer attention network for object re-identification

Microvascular maturation of the septal capillary layers takes place in parallel to alveolarization in human lungs.

Primary and secondary septa formed during lung development contain a double-layered capillary network. To improve gas exchange, the capillary network is remodeled into a single-layered one, a process that is called microvascular maturation (MVM). It takes place during classical and continued alveolarization. Classical alveolarization is defined as a formation of new septa from immature septa and continued alveolarization as a formation from mature septa. Until now, MVM was never quantitatively evaluated in human lungs. To correlate alveolarization and MVM, and to determine the transition point from classical to continued alveolarization, the degree of MVM was stereologically estimated. In 12 human lungs (0.1-15 yr), the alveolar surface area of immature and mature septa was estimated stereologically by intersection counting. An MVM-quotient (RMVM) was defined as the mature alveolar surface area over total alveolar surface area. The MVM-quotient increased logarithmically over age and showed a biphasic increase similar to alveolarization. It did not reach 100% maturity in these samples. A linear correlation between the MVM-quotient and the logarithm of the number of alveoli was observed. We conclude that MVM increased logarithmically and biphasically in parallel to alveolarization until alveolarization ceased. However, at 2-3 yr of age three-quarters of the alveolar microvasculature are mature. This result may explain a previous postulate that MVM is finished at this age. We hypothesize that as long as alveolarization takes place, MVM will take place in parallel. We propose that the transition from classical to continued alveolarization takes place between the ages of 1-3 yr in humans.NEW & NOTEWORTHY Newly formed alveolar septa contain a double-layered capillary network. To optimize gas exchange, the two layers fuse to a single-layered capillary network during microvascular maturation. Because its timing is unknow in humans, microvascular maturation was stereologically estimated throughout postnatal human lung development. It is shown that maturation of the microvascular and alveolar septa takes place in parallel to alveolarization. At an age of 2-3 yr three-quarters of the septa are mature.

Performance simulation and diagnosis of faulty states in air-cycle refrigeration systems in civil aircrafts

In the present study, performance simulation models and diagnosis method of faulty states in air-cycle refrigeration systems in civil aircrafts are developed, and the effects of the training data set on the diagnosis accuracy were analyzed. A base model for normal state simulation of air-cycle refrigeration system (ACS) was established based on the existing method with several modifications to improve accuracy and performance. Dedicated models for faulty states were developed according to the causes of fault. Based on the established models, a data set was generated. Preliminary training of multiple two-layer neural network models is performed with the existing data set, and the trained model achieved a validation accuracy of 99.00 % which confirms the feasibility of using double-layer neural network for fault identification and classification based on simulation data. The effects of the training data set to the model are analyzed, and it was concluded that: a data set of about 50,000 entries can be used to train a satisfactory diagnostic model; the proportion of normal state data needs to be reduced appropriately to improve the sensitivity of the model to faulty states; it is possible to largely reduce the number of sensors with smaller effect to the diagnosis accuracy.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies: A Double Layer Network Approach

High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains' operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains' operation strategy and the systems' constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Characterization of the physiochemical properties, microstructure, and molecular interactions of a novel rice-pea protein gel.

The application of rice and pea proteins in food production is limited due to their undesirable processing performance. The objective of this research was to develop a novel rice-pea protein gel using alkali-heat treatment. This gel had a higher solubility, stronger gel strength, better water retention capacity, and denser bilayer network. This is due to the alkali-heat induced modifications for the secondary structures of proteins (i.e., a decrease in the α-helix, and an increase in the β-sheets) and the interactions between protein molecules. The network structure of gel was more compact by adding 2% and 4% alkali-heat rice protein (AH-RP). This resulted in a stable double-layer network structure of gel. Adding 4% AH-RP significantly improved the hardness and elasticity of gel. This gel will have a good potential use for being the ingredient to produce the functional foods and meat analogs.

Small samples noise prediction of train electric traction system fan based on a multiple regression-fuzzy neural network

The fan noise in the train electric traction system contributes to the main noise of the train, affecting the comfort of the passengers. The prediction of fan noise can serve as a foundation for the design and fault diagnosis of the fan in the train electric traction system as well as noise reduction. This paper proposes a prediction model based on multiple regression analysis and a double-layer fuzzy neural network (MRA-2LFNN). After the multiple regression analysis of the train electric traction system fan noise is performed to determine the prediction input, a fuzzy neural network is used to predict the fan noise. Afterward, further analysis of noise influencing factors is presented based on the experimental results, and the possibility of the fan optimal design is discussed. This method can make accurate predictions using a small sample of fan noise data, reducing the need for data samples and lowering costs. The experimental results show that the proposed method can achieve better prediction results using the small sample dataset by training and verifying the actual train electric traction system fan noise data, and the average prediction accuracy rate is 94.15%, After discussion, when the number of fan blades is 16, the train electric traction system exhibits improved noise performance.

Silicon/Carbon Nanoparticles Assembled with Multifunction Carbon Nanotubes/Sheets as High-Performance Anode of Lithium-Ion Batteries

Silicon as an electrode material in the lithium-ion battery application scenario has been hindered by its significant volumetric expansion and intricate synthesis processes. In this research, we have successfully synthesized Si@C/carbon nanotubes/carbon sheets (Si@C-CNTs/CS) composites by employing a simple one-pot method along with modified magnesium thermal reaction, which involves melamine to prevent high temperature. The resulting multifunctional Si@C-CNTs/CS composites demonstrate enhanced stability during volume change in silicon, resulting in both higher capacity compared to conventional carbon coating layer and improved conductivity of the materials. The results indicate that the Si@C-CNTs/CS composites exhibit a high discharge-specific capacity of up to 2981.64 mAh g−1 at 0.5 A g−1 current density and retain a discharge-specific capacity of 1487.71 mAh g−1 even after 300 cycles. Therefore, the double-layer carbon network structure of carbon nanotubes/carbon nanosheets can provide an efficient and simple preparation method for high-performance Si-base anode materials in practical applications.

Identification of Changing Personnel with Double-Layer Network Fusion and Bi-Level Monitoring Mechanism

Person re-identification (Re-ID) is an essential part of visual surveillance that aims to identify and locate persons from multiple network cameras without conflicting viewpoints. Although significant advances have been made in recent years with the use of deep learning, there are still many challenges in vision such as occlusion, exposure, background clutter, misalignment, scale, perspective, low resolution and illumination, and cross-camera methods. Dressing redefinition is a hot topic in education right now. Most existing methods assume that people's clothes do not change in a short time, but they do not apply when people change clothes. Accordingly, this article introduces a double-layer garment changer re-identification network that integrates the secondary care process through clustering and fine-grained knowledge in space and training the garment classification branch to increase the sensitivity of the network to garment characteristics. In this method, auxiliary equipment such as human bone is not used and the complexity of the model is greatly reduced compared to other methods. This article runs experiments on the famous redefined PRCC data and large-scale long-term dataset (LaST). Experimental results show that the method in this article is superior to existing methods.

Double-layer Planning of Distribution Network considering Flexible Supply and Demand

To improve the flexibility of the system, flexible resources are added to the planning of renewable energy systems for collaborative optimization. Firstly, three quantitative indicators of flexible resources are introduced to build a distributed double-layer distribution network planning model that considers flexible resources and a more detailed cost model is considered based on existing research. Secondly, the scheduling and optimization of controllable loads are coordinated to achieve a fully integrated configuration of the distribution network. Then, the APSO algorithm is used to solve the problem. Finally, test results verify that the proposed planning approach is effective in improving system flexibility, economy, and reliability.

Cooperation in an uncertain environment: The impact of stakeholders' concerted action on collaborative innovation projects risk management

Due to the impact of the epidemic, the risk management strategies of stakeholders in the project have undergone significant changes. Recently, more and more companies have adopted collaborative innovation to develop new businesses. While in an uncertain environment, it is difficult for stakeholders to generate complete trust to integrate resources to achieve systematic management of project risks. Concerted action by stakeholders becomes a viable solution to this problem. Based on collaborative theory and risk management lifecycle, this paper proposes a stakeholder-risk integration analysis framework and builds a double-layer network model to verify it. A case study was conducted, including parallel simulation experiments with different concerted action strategies. The results show that project risks can be effectively controlled when stakeholders take concerted actions to deal with the risks. Under the conditions of complex stakeholder and risk relationships in collaborative innovation projects, stakeholders can achieve systematic risk control through behavioral cooperation without devoting their personal resources, which is beneficial to protect stakeholders' interests. Our research will contribute to the theory by promoting risk management research from the perspective of resource integration to organization and action integration. Finally, we make recommendations for management and future research.

Cascading failure and recovery of metro–bus double-layer network considering recovery propagation

Cascading failure and recovery of metro–bus double-layer network considering recovery propagation

Composition design of high-performance copper alloy by coupling artificial neural network and genetic algorithm

To investigate the complex nonlinear relationship between the composition and properties of high-performance copper alloys, a double-layer artificial neural network model based on a genetic algorithm (ANN-GA) was developed using MATLAB tools. The dataset showed that the correlation coefficient of the model was greater than 95%. Quantitative calculation formulas for the alloy composition, ultimate tensile strength (UTS), and conductivity were established using the transfer function, weights, and thresholds of the ANN-GA model. The effects of the main elements on the mechanical and electrical properties of the alloys were analysed. The simulation results were similar to the experimental results obtained by the ANN-GA, confirming the excellent prediction ability of the model. Four advanced copper alloys with high strength and conductivity were successfully simulated to meet the targets of UTS (≥540 MPa) and conductivity (≥80% IACS). Two alloys were selected for melting, heat treatment, and performance testing. The error between the experimental results and predicted results was within 5%, demonstrating the applicability of the model. The proposed framework is useful for designing high-performance Cu alloys.