Scheme For Networks Research Articles

Research on reactive power voltage control strategy of distribution networks containing distributed PV

Abstract With the large-scale increase in photovoltaic power generation, its integration into the grid will affect the system voltage of the distribution network. This paper presents a reactive voltage control strategy for the distribution network based on deep reinforcement learning. Firstly, the objective function model is established based on the node voltage and network loss of the distribution network. Secondly, each photovoltaic inverter is modeled as an agent of the enhanced learning environment. The collaborative control problem of the photovoltaic inverter is transformed into an observable Markov decision-making process through the distribution network partition. The paper uses a double delay depth deterministic gradient algorithm to solve the problem. Finally, combined with the improvement of 33 nodes for verification, the proposed strategy has the stability of improving voltage, presenting an ability to effectively reduce network loss and verify the effectiveness of the proposed strategy.

A Pseudo-Measurement Modelling Strategy for Active Distribution Networks Considering Uncertainty of DGs

A Pseudo-Measurement Modelling Strategy for Active Distribution Networks Considering Uncertainty of DGs

Double layer optimization control strategy for distribution network based on population algorithm and demand optimization

Double layer optimization control strategy for distribution network based on population algorithm and demand optimization

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classification of NDs has been thoroughly explored. However, accurately classifying multiple classes of NDs remains a challenge due to the complexity of disease subclass. In our study, we develop a heterogeneous neural network (H-Net) that integrates sMRI and fMRI modes for classifying multi-class NDs. To account for the differences between the two modes, H-Net adopts a heterogeneous neural network strategy to extract information from each mode. Specifically, H-Net includes an multi-layer perceptron based (MLP-based) encoder, a graph attention network based (GAT-based) encoder, and a cross-modality transformer block. The MLP-based and GAT-based encoders extract semantic features from sMRI and features from fMRI, respectively, while the cross-modality transformer block models the attention of two types of features. In H-Net, the proposed MLP-mixer block and cross-modality alignment are powerful tools for improving the multi-classification performance of NDs. H-Net is validate on the public dataset (CNP), where H-Net achieves 90% classification accuracy in diagnosing multi-class NDs. Furthermore, we demonstrate the complementarity of the two MRI modalities in improving the identification of multi-class NDs. Both visual and statistical analyses show the differences between ND subclasses.

An efficient certificateless blockchain-enabled authentication scheme to secure producer mobility in named data networks

Named Data Networking (NDN) aims to establish an efficient content delivery architecture. In NDN, secure and effective identity authentication schemes ensure secure communication between producers and routers. Currently, there is no feasible solution to perform identity authentication of mobile producers in NDNs. Identity authentication schemes in other networks are either weak in security or performance, such as privacy leakage, difficulty to establish cross-domain trust, and long handover delays, and are not fully adaptable to the security requirements of NDNs. Additionally, the mobility of producers was not fully considered in the initial design of NDNs. This paper first revises the structure of packets and routers to support the identity authentication and mobility of producers. On this basis, this paper proposes a secure and efficient certificateless ECC-based producer identity authentication scheme (CL-BPA), which includes initial authentication and re-authentication, aimed at achieving rapid switch authentication and integrating blockchain to solve single-point failure issues. Using the Canetti and Krawczyk (CK) adversarial model and informal security analysis, the proposed CL-BPA scheme is demonstrated to be resistant to anonymity attacks, identity forgery attacks, and man-in-the-middle attacks. The performance analysis demonstrates that the proposed CL-BPA scheme exhibits excellent capabilities in terms of computation delay, communication cost, smart contract execution time, average response delay, and throughput.

Hybrid quantum key distribution: A novel approach for secure end-to-end communication in network infrastructure

The Quantum Key Distribution (QKD) network approach has emerged as a promising solution and has garnered significant interest across various domains. QKD networks can be broadly categorized into two main types: those based on measurement-device-dependent (MDD) protocols and those based on measurement-device-independent (MDI) protocols. A novel network scheme is proposed combining these two protocols to guarantee end-to-end secure communication. It comprises two interconnected networks — the first uses a measurement-device-dependent protocol to connect routers with nearby nodes, while the second employs a measurement-device-independent protocol to interconnect the routers. In the first network, the process begins with Alice (Router) generating two types of weak quantum signals — the first has intensity, frequency and polarization or phase randomly varied, while the second with different delay times. Alice randomly transmits either signal states carrying information or decoy states with varying properties to the receivers (Bobs) over the quantum channels. She also sends a synchronization signal along with the quantum signals. At the receiver end, the synchronization pulse timestamps the incoming quantum signals. Each Bob then randomly shifts the frequencies of the received signals and measures their phase/polarization of the first signal and measures the delay times of the second signal. The secret key is derived by combining the frequency, the delay time and the phase/polarization information of the quantum signals, resulting in longer key lengths. Encoding information across multiple degrees of freedom enables extended key generation for secure communication. However, between the routers, MDI-QKD protocols are used to securely share the keys. This hybrid approach aims to ensure secure end-to-end communication across the network.

A secure addressing mutual authentication scheme for smart IoT home network

A secure addressing mutual authentication scheme for smart IoT home network

A double-layer optimization strategy for distribution networks considering 5G base station clusters

The reliability of the power supply for 5G base stations (BSs) is increasing. A large amount of BS backup energy storage (BES) remains underutilized. This study establishes a double-layer optimization distribution network (DN) considering BS clusters. An energy consumption characteristics and scheduling ability model of the BSs was established to address the differences in the characteristics of different traffic flows. A double-tier planning model for BS-joining grid market ancillary services is proposed. The upper-layer model addresses optimal tidal flow problems in DNs to minimize integrated operating costs, while the lower-layer model focuses on BES economic optimization. The double-layer model changes into a single-layer linear model using the Karush–Kuhn–Tucker (KKT) condition and the Big M method. Simulation validation using the IEEE-33 node DN proves that this approach can reduce DN operating costs, regulate voltage fluctuations, and guarantee economical and safe DN operation.

Comparative Analysis of Leak Detection Methods Using Hydraulic Modelling and Sensitivity Analysis in Rural and Urban–Rural Areas

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study’s findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints.

2D and 3D Printable Self-Healing Hydrogels Based on Polyacrylic and Tricarballylic Acids through a Double Network Strategy

2D and 3D Printable Self-Healing Hydrogels Based on Polyacrylic and Tricarballylic Acids through a Double Network Strategy

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples.

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

Discovery of cycloheptapeptides phakefusins A−E from the marine sponge Phakellia fusca based on molecular networking

Guided by a probe-based molecular networking strategy, five undescribed cycloheptapeptides, phakefusins A−E (1−5), were isolated from the marine sponge Phakellia fusca. Compounds 1 and 2 contain the nonproteinogenic amino acid residues of dioxindolyalanine (Dioia) and β-3-oxindolylalanine (Oia), respectively. Compound 3 possesses a unique methionine sulfoxide, whereas compound 5 includes a glutamic acid ethyl ester unit. Their structures were elucidated through NMR spectroscopy, HR-MS/MS analysis, and the advanced Marfey's method. By synthesizing the (S, S/R)-Oia standard through tryptophan oxidation, we determined the configuration of this amino acid in compound 2 using the advanced Marfey's method. These cycloheptapeptides were evaluated for their antitumor, antibacterial, and antioxidant activities. Compound 1 showed moderate cytotoxicity against MCF-7 and PC9 cells, with IC50 values of 6.8 and 9.6 μM, respectively, while compounds 2−5 demonstrated potential antioxidant effects by upregulating HO-1, NQO1, and SOD2 levels, as well as inducing Nrf2 activation.

Distributed optimization control strategy for distribution network based on the cooperation of distributed generations

AbstractAiming to improve the voltage distribution and realize the proportional sharing of active and reactive power in the distribution network (DN), this article proposes a distributed optimal control strategy based on the grouping cooperation mechanism of the distributed generation (DG). The proposed strategy integrates the local information of the DG and the global information of the DN. Considering the high resistance/reactance ratio of DN, distributed optimization control strategies for node voltage control and active power management are developed with the consensus variable of active utilization rate. And distributed strategy for reactive power management is proposed with a consensus variable of reactive utilization rate. The convergence of the distributed control system for each group is proved. The validity and robustness of the proposed strategy are verified by several simulations in the IEEE 33‐bus system.

Analyzing the impacts of cultural backgrounds on migrants' acculturation strategies in mobile phone social networks

By employing a large-scale mobile phone dataset and constructing a social network with 6,351,033 nodes and 14,735,425 ties, this research empirically studies how the individualistic and collectivistic (I-C) cultural backgrounds of migrants influence their acculturation strategies under different contexts. We introduced the rice theory and developed new network-based constructs to measure their acculturation strategies, I-C cultural backgrounds, and socioeconomic status. We found that migrants with more collectivistic cultural backgrounds tend to interact more with people from their hometowns rather than locals of their host societies. Surprisingly, this relationship is contingent on cultural distance: when a migrant's cultural background is similar to the host society, this migrant tends to interact more with local friends than people from her hometown. Moreover, higher socioeconomic status is found to decrease the cultural conservatism tendencies of migrants with more collectivistic cultural backgrounds. These findings can help stakeholders to devise more refined policies to better integrate migrants with diverse backgrounds and reduce potential cultural conflicts.

Hybrid differential evolution algorithm for Falkner-Skan flow with rotation

A hybrid differential evolution algorithm is used in this work to study the rotating transport of Falkner-Skan flow. The problem is modeled as an equivalent optimization problem by using Padé rational approximation functions. The primary model of the governing partial differential equations is imposed to subjugate the error between profiles. A hybrid evolutionary algorithm based on differential evolution and a convergent version of Nelder-Mead direct search algorithm is employed to perform global exploratory search along with an enhanced exploitation to improve the accuracy of the proposed solution scheme. The resulting scheme is named as evolutionary Padé approximation (EPA) scheme. The performance of the proposed EPA scheme on the Falkner-Skan boundary value problem is investigated by considering various values of the rotation parameters. The developed optimizer in EPA scheme was able to minimize the residuals up to10−10. Results are displayed graphically in order to study the effect of various types of parameters. EPA scheme determined that angular velocity increases or decreases accordingly as fluid parameter (β) and the rotation parameter (λ) but shows inverse behavior with respect to power law index(n). Similarly, the response of velocity profile along y−axis was decreasing function of β as well as n but increasing function of λ. The performance of the proposed EPA scheme has been demonstrated by comparing results with a hybrid neural network scheme and found in excellent agreement.

Accurate and Efficient Schemes for Mapping Out Two Isolated Seams of Conical Intersections with Neural Networks Solely Based on Adiabatic Energies: A Case Study of H+ + NO(X2Π) → H + NO+(X1Σ+).

A new scheme in the neural network (NN) diabatization approach that solely utilizes the adiabatic energies for constructing the global diabatic potential energy matrices (PEMs) of the molecular systems with two isolated seams of conical intersections (CIs) is proposed. Taking a prototype charge transfer reaction H+ + NO(X2Π) → H + NO+(X1Σ+), where two seams of CIs are located at the different linear geometries N-O-H and O-N-H, for example, the diabatization with the new scheme including a diabatic state constraint is shown to map out the topographies of both two linear CIs with 100% of the success rate in 10 different trainings, while the diabatization without such constraint hardly represents CIs, in which the avoided crossings appear instead. Simultaneously, we propose a scheme to separate the whole reactive space into three different regions and define the minimal Euclidean distances for each region to efficiently sample the energy points for the NN trainings. Through adjusting the minimal Euclidean distances, the number of the adiabatic energy data needed in the construction of diabatic PEM can be heavily decreased, lowered from ∼2000 to ∼280 energy points, which is much less than the number (>22 000) of ab initio energy points used in earlier spline diabatic PEM. Further quantum dynamic calculations show that the reaction probabilities, vibrational state distributions, and vibrational state resolved differential cross sections are well reproduced on the new NN diabatic PEM, validating these schemes for constructing the diabatic PEM.

Voltage optimal control model of regional distribution network after reconfiguration based on multi-energy conversion

In the event of a significant operational fault in the regional distribution network connected to the distributed new energy generator set, it may be necessary to quickly reconfigured certain areas of the system. This process must ensure minimal impact on the voltage of the regional distribution network. To address this issue, a voltage optimal control strategy for regional distribution network based on multi-energy conversion is proposed. Firstly, the energy conversion and adjustable characteristics of electricity, heat, and gas energy conversion equipment in the regional distribution network are analyzed in this paper. The steady-state operation model of the regional distribution network with multi-energy conversion is established. Then, the voltage optimal control model for rapid reconfiguration of the multi-energy distribution network is established by considering the two optimization objectives of power recovery for the important load and system voltage fluctuation. The model is solved based on the greedy algorithm. Finally, an equivalent model of the regional distribution network with multi-energy conversion equipment access is built based on the operation data of the distribution network in a certain area of Northeast China. The feasibility and effectiveness of the model are analyzed and verified.

Optimizing Distributions for Associated Entropic Vectors via Generative Convolutional Neural Networks.

The complete characterization of the almost-entropic region yields rate regions for network coding problems. However, this characterization is difficult and open. In this paper, we propose a novel algorithm to determine whether an arbitrary vector in the entropy space is entropic or not, by parameterizing and generating probability mass functions by neural networks. Given a target vector, the algorithm minimizes the normalized distance between the target vector and the generated entropic vector by training the neural network. The algorithm reveals the entropic nature of the target vector, and obtains the underlying distribution, accordingly. The proposed algorithm was further implemented with convolutional neural networks, which naturally fit the structure of joint probability mass functions, and accelerate the algorithm with GPUs. Empirical results demonstrate improved normalized distances and convergence performances compared with prior works. We also conducted optimizations of the Ingleton score and Ingleton violation index, where a new lower bound of the Ingleton violation index was obtained. An inner bound of the almost-entropic region with four random variables was constructed with the proposed method, presenting the current best inner bound measured by the volume ratio. The potential of a computer-aided approach to construct achievable schemes for network coding problems using the proposed method is discussed.