Performance Of Network System Research Articles

Experimental study of rainwater grate blocking and submergence of outfall on drainage network capacity

Accurately evaluating the performance of urban underground drainage network and its influencing factors is a challenging problem, as this process is affected by many complex factors. In this study, based on an overland flow experiment considering drainage process of pipe network, a series of physical model experiments were conducted to investigate the influences of different surface slopes, rainwater grate blockage and the submergence of outfall on the performance of the drainage pipe network system. The hydrographs of surface runoff and pipe network flow were recorded in collection tanks by precise digital pressure sensors to provide comprehensive information about the characteristics of drainage performance in the pipe network. Through a series of experimental data collection and analysis, the following conclusions are drawn from this study: (1) The longitudinal slope of the road decreases the pipe drainage capacity by 1.68%–8.94%, and this reduction effect is more significant with the increase of slope. (2) The blockage of rainwater grate at different locations has different impacts on the road drainage system, the downstream rainwater grate blockage has the most obvious impact on the performance of the drainage system, which reduces the drainage capacity by 22.59%–25.38%. (3) Different submergence degrees of rainwater outlet have different impacts on the drainage system. Under different slopes, the drainage capacity of the pipe network decreases by 1.88%–23.46% with the increase of the submergence degree of the outfall. These experimental results are helpful in understanding the working conditions of urban road drainage system and the influencing factors of the system's drainage capacity, and also provide measured data for verification of relevant numerical models and coefficient calibration.

Exponentially Synchronous Results for Delayed Neural Networks With Leakage Delay via Switched Delay Idea and AED-ADT Method.

Time delay has always been one of the main factors affecting the application performance of neural network (NN) systems, and dynamic performance research of NNs with time delays has been the focus of many scholars in recent years. This article enquires into the exponentially synchronous problem of switched delayed NNs with time delay in the leakage term. Adopting an unusual form from a common switched system, the switching modes of the switched delayed NNs system in this article are dependent on time delays. In the first place, the master, slave, and error NNs models are reconstructed into the switched form by introducing the switched delay idea. Then with the help of the admissible edge-dependent average dwell time (AED-ADT) method and delay-dependent switching adjustment indicators, a novel set of generalized delay-mode-dependent multiple Lyapunov-Krasovskii functionals (MLKFs) is built for analyzing the cases where a state-feedback controller exists and does not exist in the model, and where parts of LKFs may increase during the period when the corresponding subsystems are activated. For these cases, several effective exponential synchronization criteria and switching laws are presented accordingly. At last, the verification of the theoretical results is shown through a few examples.

Optimal Tracking Performance of Networked Control Systems Under Communication Channel Noise

Optimal Tracking Performance of Networked Control Systems Under Communication Channel Noise

The Impact of Reduced Non-technical Distribution Losses on GHG Emissions by Implementing Advanced Metering Infrastructure

The distribution losses in the electrical system demonstrate the reliability and efficacy of the network in providing electricity to the customers. The PLN Statistical Report 2022 stated that 20,236 GWh of electricity became losses at PLN’s distribution network across Indonesia. Not only causing the financial disadvantage, but these losses are also believed to have an adverse effect on the environment since they raise the amount of greenhouse gas (GHG) emissions because energy losses need to be compensated. Thus, various initiatives are conducted to improve the performance of distribution network system, including in Bali. In attempt to minimize losses, PLN Bali Distribution Unit has gradually implemented Advanced Metering Infrastructure (AMI) technology throughout 2023. This research is supposed to examine the impact of the AMI installation on the value of losses and GHG emissions. Our findings suggest that AMI technology has a positive impact on non-technical losses but an insignificant impact on total losses, defying the widely held belief that it can notably reduce losses. The environmental impact then can be quantified by converting the losses value to GHG emissions.

Optimal tracking performance of networked control systems under communication constraints

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output discrete-time communication-constrained systems by thinking about Denial of Service (DoS) attacks, codecs and additive Gaussian white noise under energy constraints. The non-cooperative relationship between DoS attacks and intrusion detection systems (IDS) is analyzed using repeated game theory. A penalty mechanism is constructed to force the attackers to adopt a cooperative strategy, thus improving the system performance. Partial factorization and spectrum decomposition are used to provide the OTP for systems. The results demonstrate that the systems’ OTP are linked to intrinsic characteristics like non-minimum phase zeros and unstable poles. Finally, concrete examples are shown that the results are accurate.

Development of Model Optical Distribution Point Location Detector System with GIS Approach Method- Case Study in PT. Telkom Witel Bandung

Principally, the validation of the location of the Optical Distribution Point (ODP) device is carried out to match the field data with the data at the center of the system. The current problem is that there are frequent shifts in ODP location data points in the field with the data system at PT. Telkom Witel Bandung because the existing detection system is still manual. This condition directly disrupts the performance of the communication network system so that service to customers is reduced. Therefore, it is necessary to develop a digital and automatic detection system for detecting ODP locations through the use of the Geographic Information System (GIS) method. This method is able to increase the accuracy and efficiency of the travel time to the ODP location point. This study aims to increase the accuracy of the ODP device at different locations through the development of a location point detection system using the geographic information system method approach. The testing technique carried out in this study used two methods, namely the direct method and the GPS field measure method. The test results on the developed geographic information system have been successfully integrated with Google maps and the GPS sensor has been successfully sent to the application. The results of the previous system obtained a location accuracy rate with an error percentage value of 3.6%. While the results of this development system obtained an error percentage value of 1%. Based on the results of the final test, it shows that the system developed in this study produces a better accuracy value than the previous system. In the future, this developed system can be implemented to accurately detect ODP locations for larger numbers and larger areas.
  

The I/O Driven Server: From SmartNICs to Data Movement Controllers

Many researchers are turning to SmartNIC offloads to improve the performance of high-performance networked systems. In this editorial, I discuss why SmartNICs are an especially powerful form factor for improving I/O intensive applications, and how their position in the dataplane enables them to take on central role in managing I/O. Rather than focusing on the benefits of individual offloads, this paper aims to explore the position of SmartNICs in the overall system integration of datacenter servers at the hardware and software level. I argue that SmartNICs should be viewed as 'data movement controllers' (NIC-DMCs) which are responsible for tasks involved in moving data between network, CPU, accelerators, and other endpoints: multiplexing/steering, interfacing between protocols, and enforcing I/O policies. I then enumerate open questions in how the hardware and software systems of the future will evolve to accommodate a dedicated NIC-DMC which is independent of the CPU complex.

Channel Assignment Method for Maximizing Throughput in the Internet of Things System

The growth of the number of interconnected wireless devices such as the Internet of Things (IoT) is continuously increasing across various sectors, including smart buildings, smart offices, smart cities, and others. According to estimates, by the year 2030, there will be at least 50 billion devices interconnected through networks. The escalating number of uncontrolled wireless devices can lead to various issues such as interference, collisions, and data loss, resulting in an overall decline in network system performance. This study aims to propose a scenario as an alternative solution to optimize the overall network performance in the system by assigning channels to each interconnected wireless pair to reduce the impact of interference. This research indicates that the proposed method successfully enhances the system throughput performance by 39.75% compared to the condition where all wireless pairs operate on the same channel, thereby outperforming several other comparative methods.

Quantitative analysis of the importance and correlation of urban bridges and roads in the study of road network vulnerability

The city development is closely related to the performance of the transportation network system. Bridges and roads are important parts of the transportation system, and are also inseparable components of the transportation network. However, the effect of the correlation between bridges and roads on the network system has not been studies thoroughly in the literature. Therefore, it is necessary to analyse the vulnerability of the road network when both bridges and roads are involved. In this paper, the urban road network is modeled into the form of network connection and node, based on the analysis of the related research results of road network vulnerability in the literature. Taking the urban roads at all levels as the connection and the transportation hubs (including bridges) as the nodes, the paper puts forward the corresponding measurement indexes and calculation methods, and establishes the importance and correlation analysis model of roads and bridges in the urban road network. At last, the model is applied to the road network which is 5 × 3 km2  besides Yangpu Bridge of Shanghai for verification, the importance and correlation of specific roads and bridges in the analyzed urban road network are calculated, which provides a certain basis for dealing with various emergencies leading to the decline of urban road network vulnerability. In this paper, the importance analysis of urban road network is extended to the bridge correlation analysis, so that the proposed model of the vulnerability assessment of the urban road network system is more suitable for the increasingly demand of road and bridge construction in China, and provides a certain basis for dealing with the decline of road network vulnerability caused by various emergencies.

Can Decentralized Control Outperform Centralized? The Role of Communication Latency

In this paper, we examine the influence of communication latency on performance of networked control systems. Even though distributed control architectures offer advantages in terms of communication, maintenance costs, and scalability, it is an open question how communication latency that varies with network topology influences closed-loop performance. For networks in which delays increase with the number of links, we establish the existence of a fundamental performance trade-off that arises from control architecture. In particular, we utilize consensus dynamics with single- and double-integrator agents to show that, if delays increase fast enough, a sparse controller with nearest neighbor interactions can outperform the centralized one with all-to-all communication topology.

Energy balanced routing protocol based on improved particle swarm optimisation and ant colony algorithm for museum environmental monitoring of cultural relics

AbstractThe environmental monitoring of cultural relics based on wireless sensor networks in museums demands for the transmission and processing of massive data, which in turn leads to problems, such as heavy network traffic, high time delay, and unbalanced node energy consumption. To solve these problems, an energy balanced routing protocol which can minimise the network energy consumption is proposed. The improved swarm intelligence optimisation algorithm which combined particle swarm optimisation with ant colony optimisation is adopted to construct an environmental monitoring system for cultural relics preservation. Simulation results show that the decrease of the path length for data transmission under the proposed algorithm is 2.5% higher than that of other classical algorithms, indicating that the proposed algorithm can effectively reduce the length of network path and improve the transmission performance of network system. In view of optimisation rounds and simulation time, the energy consumption of the network system under our algorithm is 57.5% less compared with other classical algorithms, and the average residual energy variance is 57.1% less. The cultural relics environmental monitoring system based on the proposed routing protocol has better network load balancing performance.

Co-Design of Adaptive Event Generator and Asynchronous Fault Detection Filter for Markov Jump Systems via Genetic Algorithm.

This article investigates the co-design problem of adaptive event-triggered schemes (AETSs) and asynchronous fault detection filter (AFDF) for nonhomogeneous higher-level Markov jump systems, involving the hidden Markov model (HMM), higher-level Markov chain (MC), and conic-type nonlinearities. The transformation of the system transition probability can be reflected by the designed higher-level MC. An HMM with another conditional transition probability is applied to detect higher-level Markov processes and make the system be more practical. In order to balance the utilization of network resources and system performance, a novel AETS is proposed and used in the construction of the AFDF. By the Lyapunov theory, sufficient conditions are given to ensure the existences of the AETS and AFDF. It is not only an appropriate tradeoff between the utilization of network resources and system performance, but also reduces the conservatism. Finally, a numerical example is given to detect the faults effectively by the co-designed AFDF.

Optimized Deep Reinforcement Learning for Smart Charging Scheduling of Plug-in Electric Vehicles

In recent decades, fossil fuels have been the major source of energy but concerns about price fluctuations, security, and environmental issues. Plug-in electric vehicles (PEVs) should be connected to the power grid for battery charging. With a wide range of cars, the power system performance in the distribution network. The uncontrolled charging of the battery can cause issues such as overvoltage, overload, unbalanced load, instability, harmonics, and increased loss. Hence, efficient charging methodologies need to be developed for the power grid. The objective of this article is to maximize the total revenue of the charging stations and schedule the charging stations for PEV charging with reduced cost. First, the system model with its objectives such as demand, constraints, and fitness values are computed. Next, efficient charging path planning is proposed using the deep Q network, which is a reinforcement learning model. This can assist the PEV user in selecting the charging station and planning its path. Lastly, the particle sailfish optimization from our previous research work has been used to solve the charging station optimal solution. The simulation results analysis shows the efficiency of the proposed model in reducing the charging cost and time and improves the charging scheduling of PEVs.

Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

AbstractOff-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.

Interference Analysis in Non-Poisson Networks Under Spatially Correlated Shadowing

Spatial correlations that appear in mobile wireless networks, such as the correlations of node locations and shadowing effects, significantly affect the characteristics of interference, which may degrade the performance of various wireless network systems. In this paper, we theoretically analyze the statistical and temporal characteristics of interference in a network where the node locations and shadowing are spatially correlated. We model the correlation of the node locations by two types of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-Poisson</i> point processes: determinantal point processes (DPPs) for modeling <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repulsiveness</i> and doubly Poisson cluster processes (DPCPs) for modeling <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attractiveness</i> . Furthermore, we consider Gudmundson's model for the spatial correlation of shadowing. Using this model and assuming an i.i.d. mobility of nodes, we analyze the variance along with the spatial and temporal correlations of interference. Since the exact expressions are in non-analytical forms, we derive their simple closed-form asymptotic formulas when the variance of the shadowing is large. The results show a readable relationship between the characteristics of interference and various system parameters. This relationship can be used for realistic modeling and better understanding of various wireless network systems under spatially correlated shadowing. Moreover, we discuss various numerical examples and demonstrate that the obtained asymptotic expressions achieve tight approximation.

Optimal switching control design for industrial networked control system with uncertain exogenous dynamics

Industrial networked control system performance can be compromised through introduction of intended attacks by intruders. Several ways have been reported in literature to introduce attacks like denial-of-service, packet dropout, and delays etc. in Industrial networked control system. Attacks may be designed based on the system knowledge gathered through eavesdropping process. This article deals with problem of constrained optimal switching control design with industrial networked control system (iNCS) through avoiding performance deteriorating uncertain exogenous dynamics. To characterize and detect the abnormal behaviour of iNCS a random distribution process is adopted which represent the attack sequence in the process on the plant side. The delays and packet loss are introduced through Bernoulii distribution process to express uncertainty of network and also attack introduced by intruders on either side of tightly coupled component. The iNCS performance is evaluated with uncertain exogenous dynamics i.e. packet loss 0%, 10%, 20%, 30%, delays, and malicious attack. The proposed design guarantees the transient and steady state performance of linear-time-invariant iNCS. The proposed design methodology evaluated under switching of optimal predictive control performance of networked system with simulation of numerical problem.

Deep learning strategies for addressing issues with small datasets in 2D materials research: Microbial Corrosion.

Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.

Water Quality Modelling of River Periyar Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

Water is requisite on earth for life to survive. The study area of the present work is the river Periyar, the longest river in Kerala, India. The overall objective of the present work is to compare the accuracy and performance of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in the training and prediction of dissolved oxygen (DO) concentration and biochemical oxygen demand (BOD) in the river. The models were used to examine secondary data from the past for four water quality metrics generated at five monitoring stations located along the river Periyar to predict DO concentrations and BOD. The performance of the models was examined using root mean square error (RMSE) and coefficient of correlation (R) values. This study revealed that ANN is superior to ANFIS in most scenarios. In most situations, ANFIS appears to get the superiority in training, however, ANN seemed to have the dominant position in testing and validation. The inputs having the highest relevance in the case of DO prediction were identified to be nitrate and phosphate concentrations, whereas total solids and COD were seen to be the most impactful in the case of BOD forecasting employing sensitivity analysis.

Phase stochastic resonance in coherent QKD signal transmission with a crosstalk noise in multicore fiber

The study discusses a linear co-propagation of the quantum signal of the Quantum Key Distribution (QKD) system and multiple classical signals in the multicore fiber. The crosstalk from the neighboring core produces noise in the quantum channel. For the first time, the phase stochastic resonance was shown in the quantum network system. The dependence of SNR vs phase noise of crosstalk signal could have a maximum at a certain range of phase noise. The maximum depends on the wavelength of the classical signals that produce the crosstalk. A set of transmission parameters that are close to the SNR maximum can be chosen that will improve the performance of the quantum network systems.

Tracking and Regulation Performance Limitations of Networked Control Systems Over Erasure Channel With Input Quantization

Limitations in communication networks, such as delays, packet dropouts, and insufficient bandwidth, create great challenges in the output tracking control and regulation performance of networked control systems. In this article, we propose some novel performance indexes for achieving the optimal tracking and regulation performance. By considering the influences of noise, packet dropouts, and logarithmic quantization on the channel input, the performance limitations of the networked systems are investigated by applying a two-degree-of-freedom (TDOF) control technique. The results obtained prove that the tracking and regulation performance is dependent not only on the position of the plant’s nonminimum phase zeros and unstable poles, but also on the directions. Furthermore, it is demonstrated that noise and quantization error have a negative influence on the control performance, and the design of the TDOF controller can eliminate the effect of packet dropouts on control performance. To validate the innovation results, several simulations are conducted.