Network System Research Articles

Message-passing approach for percolation on the networked system: A mini-review

Network percolation is one of the core topics in network science, especially in understanding and optimizing the robustness of real-world networks. As a powerful tool, the message-passing approach shows unique advantages in characterizing network percolation compared with the mean-field approach. This approach simulates the behavioural response when the network is damaged by transmitting and updating messages between network nodes, thereby accurately assessing the robustness of the network. This paper reviews the progress of message-passing approaches in network percolation on simple networks, multilayer networks and higher-order networks in recent years and discusses the application of this approach in other research fields. Finally, we discuss future research directions around this approach.

An Innovative Approach for Enhancing Relay Coordination in Distribution Systems Through Online Adaptive Strategies Utilizing DNN Machine Learning and a Hybrid GA-SQP Framework

AbstractThe present study addresses the issue of varying fault locations within a distribution system, which leads to fluctuations in short-circuit currents and requires the implementation of adaptive protection strategies for network reliability. This paper presents a novel adaptive protection scheme that specifically considers these fault location variations using directional overcurrent relays (DOCRs). Unlike previous research on adaptive protection, which does not adequately account for fault location variations, this method employs deep neural networks (DNNs) for online fault location detection. In the verification process, the effectiveness of the proposed methodologies was assessed by analyzing the time derivative of a trained machine learning model for fault identification. This approach enables the immediate detection of fault locations within the distribution system and facilitates the transmission of the setting group index to activate preset optimal coordination parameter values for the system relays. Crucially, the proposed method ensures that the coordination constraints remain intact across various adaptive settings, relying on precise fault identification through machine learning. The practical significance of this approach lies in its applicability to real-world systems because the proposed solutions and protective settings can be easily implemented using commercially available relays. To evaluate its effectiveness, the adaptive protection scheme was tested on three distribution networks: IEEE 14-Bus, 15-Bus and 30-Bus. The comparative test results highlight that the proposed method significantly improves the speed of the protection system for distribution networks when compared to existing studies, making it a valuable contribution to enhancing network reliability and performance.

Investigation of the energy performance of thermal energy sharing for photovoltaic-thermal systems in a thermal prosumer network

This study analyzes the potential for energy saving through the integration of a photovoltaic-thermal (PVT) system with a heat pump (HP) in a community with a small-scale thermal network. An empirical study was conducted in a community to analyze and compare the performance of a HP and small-scale thermal network with hub thermal energy storage system that utilizes the surplus thermal energy produced by a PVT system installed in a residential house from other buildings with that of an existing electric hot water boiler. The energy saving analysis of the proposed system revealed that the energy consumption could be reduced by more than 70% compared to the existing system. The HP exhibited a COP ranging between 4.2 and 4.7. Throughout the experiment, the integrated system collected an average of 23.77 kWh of heat energy daily from the PVT units. The annual domestic hot water load coverage from PVT through the thermal network was found to be 50.4%. These results indicate that PVT systems combined with thermal networks and HPs can effectively reduce energy consumption in individual buildings and enhance energy efficiency within communities.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.

Electric vehicle fast charging station energy management system for radial distribution network with a photo-voltaic distributed generator (PV-DG)

As the demand for electric vehicles (EVs) increases in all countries, automobile companies are launching different types of EVs. Installation of EVCS in the present power system network without modification can cause an extra burden on the present network. However, this extra burden can be reduced if EVCS gets a supply of renewable energy resources. This paper proposes energy management schemes by installing a photo-voltaic distributed generator (PV-DG) and energy storage system (ESS) at an EVCS. An IEEE-33 bus system for the Saudi Arabia United Kingdom rural location has been selected to introduce real-time energy management schemes. In this radial distribution network (RDN), three EVCS of different capacities are installed at different locations to maintain the stability of the network. The power flow program is used in the MATLAB environment to check the stability of the network. Accurate solar data, traffic patterns, and EV charging patterns in Saudi Arabia are collected and used for energy management at EVCS. Three cases are used for comparison: EVCS annual charges, EVCS annual benefits, and grid annual fuel charges. All proposed cases are also simulated in MATLAB using MATPOWER6 to check the impact on voltage profile and power line flows.

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Abstract The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

Dynamic periodic event-triggered control for nonlinear systems with output dynamic quantization

This paper is concerned with the design of stabilizing event-triggered controllers for nonlinear systems subject to dynamic quantization. The plant state is assumed to be partially known, and the feedback signal is transmitted to the controller at discrete-time instants via a digital channel. To that end, an event-triggered controller is synthesized based solely on the available plant measurement. In addition, to better adapt it to practical implementation, the event-triggering law we construct is only verified at periodic time instants, i.e., periodic event-triggering mechanism. Since the network is digital, the transmitted output signal must be quantized to be encoded using a finite number of bits. Furthermore, the initial output value is assumed to be unknown, and the plant is affected by external disturbance, which can result in quantizer saturation. To overcome these challenges, a dynamic quantization policy is developed for the output measurement such that the quantization regions are dynamically updated according to the value of the quantized signal. The combined approach of periodic event-triggering mechanism and dynamic quantization ensures the stability of the networked control system under mild conditions. The stability is studied using appropriate Lyapunov functions. Numerical simulations have been conducted to justify the proposed technique.

Digital nonlinear self-interference cancellation using SW-GRU-TPA

In order to enhance spectrum utilization and decrease transmission delay, the 6th generation mobile networks (6G) system adopts full-duplex (FD) mode. Simultaneous operation of the same frequency inevitably causes the receiver to receive the signal sent by the transmitter, leading to serious nonlinear self-interference due to the nonlinear distortion generated by the power amplifier (PA). This paper proposes hybrid layer structures SW-GRU-TPA for the implementation of digital self-interference cancellation (SIC), combined with Sliding Window (SW), Gated Recurrent Unit (GRU) , and Temporal Pattern Attention (TPA).The model can not only effectively utilize the influence of historical information on the signal, but also capture the spatial and temporal dependencies in the data to fit the nonlinear characteristics of the PA and the delay effect in multipath transmission. Meanwhile, by utilizing the L1 norm model pruning, the model parameters can be optimized, leading to improved processing efficiency. The model is verified using two methods: injection and antenna reception. Compared to the SW-GRU model with Attention Mechanism (AM), the Interference Cancellation Ratio (ICR) of injection and antenna increases by 3.13 dB and 2.01 dB, respectively. When the TPA is compressed by 42.17%, the ICR of injection and antenna decreases by 1.58% and 2.6%.

Asymptotical Mean-Square Stabilization of Networked Control Systems Operating Over Multipath Routing Networks

Asymptotical Mean-Square Stabilization of Networked Control Systems Operating Over Multipath Routing Networks

Emulation and detection of physical faults and cyber-attacks on building energy systems through real-time hardware-in-the-loop experiments

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physical faults (e.g., HVAC − heating, ventilation, and air-conditioning faults) may have similar adverse impacts as those from the cyber-attacks on building energy systems, such as occupant discomfort, energy wastage, and equipment downtime. However, current physical behavior-based anomaly detection methods fail to differentiate between cyber-attacks and physical faults in building energy systems. Moreover, the challenge in collecting real-world threat data with ground truth has led researchers to rely on numerical models with user-defined assumptions, which may not accurately reflect real-world conditions due to the lack of in-situ experimental datasets. To address these challenges and gaps, this paper presents a flexible hardware-in-the-loop (HIL) testbed for generating cyber-attack and physical fault datasets and demonstrating threat detection algorithms in a real building automation system (BAS) environment. This testbed combines hardware (i.e., real BAS with local HVAC controllers and a physical network) with software (i.e., high-fidelity models to represent behaviors of building envelope and HVAC energy systems), enabling emulations of realistic threats. Five HIL experiments, including one baseline without any threats, two with physical faults, and two with cyber-attacks, were conducted to generate datasets containing detailed network traffic and system states. A joint classification framework, incorporating a network analyzer and a physical HVAC fault detector, was proposed to automatically detect cyber-physical abnormalities on BAS at both the network and the physical HVAC levels. The network analyzer comprises a conditional random fields (CRF) based command validator and a statistics-based detection strategy. The fault detector employs a weather and schedule-based pattern matching and feature-based principal component analysis (WPM-FPCA) method. Evaluation of the classification using four metrics from the multi-class confusion matrix revealed an average accuracy of 90.2 %, recall of 89.7 %, precision of 88.5 % and F1-score of 89.2 %. These results demonstrate that the proposed joint classification framework can effectively differentiate between specific types of cyber-attacks (e.g., device reinitialization attack, network Denial-of-Service attack) and physical faults (e.g., air handling unit operational fault, cooling coil valve stuck) in real time for improved building energy management.

Dynamic sum-based event-triggered H∞ filtering for networked T-S fuzzy wind turbine systems with deception attacks

This article is concerned with the dynamic sum-based event-triggered H∞ filtering issue for networked wind turbine systems subject to deception attacks and communication delays. By considering the time-varying wind power case rather than the existing maximum power case, a more general T-S fuzzy system with two premise variables is modeled for nonlinear wind turbine system. In order to save the communication cost, a novel dynamic sum-based event-triggered scheme is proposed, which has the following three merits. First, some past sampled measurements are utilized to reduce redundant transmissions. Second, an auxiliary dynamic variable based on the past sampled measurements is introduced in the triggering condition to further enlarge the triggering intervals. Third, a dynamic triggering threshold is designed, which can be regulated adaptively along with the system evolution. With the help of Lyapunov method and linear matrix inequality technique, some sufficient co-design conditions of H∞ filter and triggering matrices are derived for the T-S fuzzy filtering error system with deception attacks and communication delays. Lastly, some simulations are carried out to illustrate the advantages of the proposed strategy.

Dynamics of diseases spreading on networks in the forms of reaction-diffusion systems

In the face of persistent threats posed by infectious diseases, despite remarkable medical advancements, understanding and efficiently controlling their spatial spread through mathematical modeling remain imperative. Networked reaction-diffusion systems offer a promising avenue to effectively delineate population discrete distribution and individual movement heterogeneity. However, the dynamics of spatial diseases within these systems and the formulation of optimal control strategies are currently undergoing vigorous development. In this letter, we illustrate the dynamics of spatial disease spread in networked reaction-diffusion systems through the lens of optimal control, considering various network complexities from pairwise networks to higher-order networks. It then emphasizes their applicability in designing effective spatial disease control strategies across diverse network complexities. Finally, we discuss the existing challenges.

Lag projective synchronization of discrete-time fractional-order quaternion-valued neural networks with time delays

This paper deals with the lag projective synchronization (LPS) problem for a class of discrete-time fractional-order quaternion-valued neural networks(DTFO QVNNs) systems with time delays. Firstly, a DTFOQVNNs system with time delay is constructed. Secondly, linear and adaptive feedback controllers with sign function are designed respectively. Furthermore, through Lyapunov direct method, DTFO inequality technique and Razumikhin theorem, some sufficiency criteria are obtained to ensure that the system in this article can achieve LPS. At last, the significance of the theoretical part of this paper is verified through numerical simulation.

Research on vehicle electromagnetic compatibility testing based on LTE-V2X direct communication

Abstract With the continuous development of intelligent networked vehicle technology, the electromagnetic environment inside and outside the vehicle is becoming more and more complicated. As the key technology of vehicle-road coordination, the electromagnetic reliability of V2X communication will affect the driving safety and ride comfort of intelligent networked vehicles. Based on V2X communication, this paper introduces the mainstream V2X technology and related standards at home and abroad. Taking the vehicle based on LTE-V2X direct communication technology as the object, the construction of the communication scene, electromagnetic emission, and immunity test method in the semi-dark room are studied, and the test results and matters needing attention in the test are given. This research can well support the formulation of vehicle networking standards and help improve the intelligent networked vehicle standards and regulations system.

Learning predictive control of wheeled mobile robot with communication delays

Abstract This paper proposed a learning predictive control method for wheeled mobile robots with communication delays. In consideration of the property in the Networked Control System (NCS), communication delay was divided into two types: delay from sensor to controller and delay from controller to actuator. For each type of delay, state prediction and state augmentation techniques were utilized respectively to diminish their passive impact on the control of WMR. Then receding horizon reinforcement learning approach was adopted to learn the optimal policy based on the delay compensation. Finally, simulations were performed applying the proposed method to the trajectory tracking of WMR and showed the validity of the proposed scheme.

Global ionospheric total electron content short-term forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997–2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

Global Ionospheric Total Electron Content Short-Term Forecast based on Light Gradient Boosting Machine, Extreme Gradient Boosting, and Gradient Boost Regression

Total Electron Content (TEC) forecasting using machine learning has been extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to support space communication and navigation applications. Although, a variety of machine learning methods have been evolved, still, there is a greater persistence in the prediction of ionospheric TEC due to adverse space weather impacts and the complex behavior of ionosphere. And hence, more sophisticated short-term ionospheric TEC forecasting models should be developed for better prediction accuracy. The present study investigated and evaluated the performance of three models – Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gradient Boost Regression (GBR) algorithms using the data set for 25 years (1997-2021) GNSS Earth Observation Network System (GEONET) Global mean TEC time-series data. For better prediction accuracy, the data of geomagnetic storm indices Dst and Mg-II index were also utilized in training the three models. One year of data for both high (2015) and low (2020) solar activity were used to test the three models. The prediction plots are used to visualize the level of prediction error for the three algorithms. The statistical results illustrate that the LightGBM model performed better in predicting TEC with an RMSE value of 2.25 TECU (2015) and 0.52 TECU (2020) for high and low solar activity years respectively for global mean TEC data and with an RMSE of 2.34 TECU at Japan grid point location of (34.95°N and 134.05°E). In turn the XGBoost and GBR models provide an absolute TEC forecasting with an RMSE of 2.76 and 2.59 TECU (2015) and 0.60 and 0.55 TECU (2020) correspondingly for global data and as 2.39 and 2.93 TECU for grid point location.

Physical understanding of a slow slip event longer than expected around the Nankai Trough

The Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) and the Long Term Borehole Monitoring System (LTBMS), installed above the source region of the 1944 Tonankai earthquake, revealed that crustal deformation is driven by slow slip events (SSEs) in the shallower extension of megathrust earthquakes. However, there are unresolved questions about (A) the duration of the SSE in February 2012, which was longer than expected for SSEs with similar magnitudes, and (B) the relationship of the spatial distribution of fault slip between the SSEs in February and December 2012 under the condition of drilling disturbance. To clarify these questions, we re-analyzed the pore/seafloor pressure data associated with the SSEs. Our refined fault models show that the SSE in February had a significantly slower propagation speed and a longer duration than others, while the SSE in December was comparable to others. We interpret that the difference in duration and propagation speed is related to external and internal stress perturbations, respectively. Using the ocean modeling JCOPE (Japan Coastal Ocean Predictability Experiment), we identified that the decrease and subsequent increase in seafloor pressure due to the passage of the Kuroshio meander coincided with the latter part of the longer duration of the SSE in February and its termination, respectively. This suggests that the Kuroshio meander might affect the duration of SSEs. Our refined fault model also indicates that the amount of shear stress accumulation was small before the occurrence of the SSE in February, which triggered the slow propagation of aseismic slip based on a rate- and state-dependent friction law. These results imply that we need to consider the variety of SSEs from the viewpoint of stress perturbation due to not only interaction between fault segments but also external forces from oceanographic phenomena.

Site database for national strong motion stations in mainland China

Ground motion amplification and spectral content are significantly influenced by local soil and geological conditions. This article summarizes the development of the China Site Database (CNSDB) in the China Ground Motion Flatfile project. CNSDB contains site information for 1450 strong-motion recording stations within the National Strong Motion Observation Network System (NSMONS) in China. The primary site information in CNSDB includes the time-averaged shear-wave velocity in the upper 30 m (VS30), and site class according to the seismic design code in China. These site data are derived using different methods from various data sources. Priority is given to approaches that minimize bias and dispersion relative to measurement-based results. Recommended VS30 values and site class are derived from reliable velocity profiles or field survey results whenever available. A China-specific VS30 extrapolation model is developed using 6179 engineering boreholes and is then utilized to estimate VS30 for profiles <30 m. VS30 values and site class are estimated from horizontal-to-vertical spectral ratio (HVSR) curves of earthquake recordings using machine-learning techniques. For the remaining sites without velocity profiles or sufficient ground-motion recordings for HVSR computation, VS30 values are determined as the weighted average of proxy-based estimates using geological attributes, topographic slope, and terrain categories. We also discuss the potential application of CNSDB in the development of China seismic hazard map considering site amplification factors.

Accuracy of automated forensic dental age estimation lab (F-DentEst Lab) on large Malaysian dataset

When a disaster occurs, the authority must prioritise two things. First, the search and rescue of lives, and second, the identification and management of deceased individuals. However, with thousands of dead bodies to be individually identified in mass disasters, forensic teams face challenges such as long working hours resulting in a delayed identification process and a public health concern caused by the decomposition of the body. Using dental panoramic imaging, teeth have been used in forensics as a physical marker to estimate the age of an individual. Traditionally, dental age estimation has been performed manually by experts. Although the procedure is fairly simple, the large number of victims and the limited amount of time available to complete the assessment during large-scale disasters make forensic work even more challenging. The emergence of artificial intelligence (AI) in the fields of medicine and dentistry has led to the suggestion of automating the current process as an alternative to the conventional method. This study aims to test the accuracy and performance of the developed deep convolutional neural network system for age estimation in large, out-of-sample Malaysian children dataset using digital dental panoramic imaging. Forensic Dental Estimation Lab (F-DentEst Lab) is a computer application developed to perform the dental age estimation digitally. The introduction of this system is to improve the conventional method of age estimation that significantly increase the efficiency of the age estimation process based on the AI approach. A total number of one-thousand-eight-hundred-and-ninety-two digital dental panoramic images were retrospectively collected to test the F-DentEst Lab. Data training, validation, and testing have been conducted in the early stage of the development of F-DentEst Lab, where the allocation involved 80 % training and the remaining 20 % for testing. The methodology was comprised of four major steps: image preprocessing, which adheres to the inclusion criteria for panoramic dental imaging, segmentation, and classification of mandibular premolars using the Dynamic Programming-Active Contour (DP-AC) method and Deep Convolutional Neural Network (DCNN), respectively, and statistical analysis. The suggested DCNN approach underestimated chronological age with a small ME of 0.03 and 0.05 for females and males, respectively.