Optimal Network Configuration Research Articles

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g−1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin–Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R 2) and root mean square error (RMSE), the optimal network configuration was identified as a 5–11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

Efficient configuration of heterogeneous multistatic sonar networks: A mixed-integer linear programming approach

The work undertaken in this paper pertains to the optimal spatial configuration of a heterogeneous Wireless Sensor Network (WSN) for the Area Coverage (AC) problem. Specifically, this research falls under the heading of Anti-Submarine Warfare (ASW) with an emphasis on active sonar systems and, more pointedly still, on a specific type of sensor: sonobuoys (portmanteau word formed by “sonar” and “buoy”). These buoys are further divided into three main categories: transmitter-only (Tx), receiver-only (Rx) and transmitter–receiver (TxRx). In this paper, we will therefore try to determine the geographical location of the different buoys comprising a Multistatic Sonar Network (MSN), special case of WSN, so as to maximize the overall surface area covered. To do this, we discretize an Area of Interest (AoI) into regular cells using bathymetric and altimetric data, and place a deployment position and a fictitious target at the center of each cell so that we can evaluate the network’s performance. More precisely, we are taking into account a limited number of sensors (buoys) with possible pairwise incompatibilities, variable performances, probabilistic detection models, an adverse masking effect (direct blast) as well as coastlines features. Finally, in order to solve this problem, we have developed several efficient Mixed-Integer Linear Programs (MILPs), all of which have been thoroughly tried-and-tested on a benchmark set of 100 instances derived from real elevation data. This has led us to identify an ideal model, i.e. one that is significantly better than all the others in the statistical sense.

NS+NDT: Smart integration of Network Simulation in Network Digital Twin, application to IoT networks

Network Digital Twin (NDT) and Network Simulation (NS) are two paradigms leveraging virtual representations of networks to help decision-making. These tools may seem similar or interchangeable and are often confused or opposed. However, they have their respective purposes, use cases and underlying concepts, which differ and are complementary. The goal of this article is to explore and clarify the specificity, the benefits and the limits of these two decision support tools, analyze how they complement each other and can be nicely combined. We argue that a smart integration of NS in NDT, named NS+NDT, can ease, accelerate and strength decisions for network design, deployment, operations, management and evolution. To study and demonstrate this claim, we focus on the domain of Internet of Things (IoT) solutions, where wireless networks are critical for connecting the physical assets to the Internet, but are complicated to configure for meeting the requirements of a specific application. We examine how NS, coupled with NDT, can contribute to support IoT architects and operators decisions throughout the life cycle of an IoT network. We analyze the different steps required to use NS in the context of NDT and examine how this helps remove NS barriers such as credibility and reliability. In particular, we show how NDT data enable to fine tune and customize the energy consumption models, making the simulation results more context-aware and insightful. Then, addressing the often-prohibitive simulation cost for exploring a large parameter space, we propose to associate surrogate modeling to NS+NDT. As surrogate models, we first introduce a simple ML (Machine Learning)-based surrogate model and illustrate this method with two IoT network configuration optimization use cases. Secondly, we propose a Bayesian optimization approach based on Gaussian Processes as surrogate model to further accelerate the (re)configuration decisions. We show how this method enables to select simulation scenarios that converge rapidly to the optimal solution, and allows the NDT to timely perform the adaptation. The contribution of this article is threefold. It provides (i) the first systematic analysis of the differences and potential synergies between NDT and NS; (ii) a synthetic presentation of the integration of NS and associated decision algorithms within a NDT to unlock NS accessibility and credibility throughout the life cycle of an IoT solution; (iii) a proposal for a smart and cost-efficient integration of NS in NDT via surrogate modeling, for reducing evaluation and optimization cost, paving the way to NS-augmented NDT-based dynamic adaptation and real-time optimization of IoT networks.

Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG

Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human–computer interfaces.

Virtual Coordinate System Based on a Circulant Topology for Routing in Networks-On-Chip

In this work, the circulant topology as an alternative to 2D mesh in networks-on-chip is considered. A virtual coordinate system for numbering nodes in the circulant topology is proposed, and the principle of greedy promotion is formulated. The rules for constructing the shortest routes between the two nodes based on coordinates are formulated. A technique for calculating optimal network configurations is described. Dense states of the network when all neighborhoods of the central node are filled with nodes and the network has the smallest diameter are defined. It is shown that with an equal number of nodes, the diameter of the circulant is two times smaller than the diameter of the 2D mesh. This is due to the large number of symmetries for the circulant, which leave the set of nodes unchanged. A comparison of communication stability in both topologies in the conditions of failure of network nodes is made, the network behavior under load and failures is modeled, and the advantages of the circulant topology are presented.

PREDICTION SYSTEM FOR THE INFLUENCE OF PHOSPHORUS ON WHEAT YIELD: OPTIMAL HYPERPARAMETER SELECTION

Modern studies in the application of machine learning in agrotechnology have showcased significant advancements in accurate crop yield forecasting and agricultural production optimization. This study aimed to select optimal hyperparameters for a neural network designed to forecast the impact of phosphorus on the yield of spring wheat. Tasks to achieve this goal included the selection of optimal neural network hyperparameters and deployment of the prediction model into forecasting system, ensuring accessibility and user convenience. Input dataset consisted of year, region, climatic indicators (temperature of the soil surface, precipitation, humidity), applied phosphorus, and the target variable is the yield of spring wheat. To select the optimal hyperparameters, GridSearchCV was utilized and integrated into the experiment, enabling the achievement of more precise and impactful forecasting outcomes. Experiments aimed at hyperparameter tuning identified an optimal network configuration (3 layers, 32 neurons, 300 epochs, 32 batches). Results from training showcased that this network configuration achieved the best performance, demonstrating minimal mean squared error (MSE). These results introduced a novel scientific approach in training data using this neural network model for forecasting the yield of cereal crops. Developing the prediction system's interface relied on the Streamlit, allowing the creation of an intuitive and user-friendly interface. The visualization derived from this model empowers users to evaluate crop yield and receive recommendations for optimal phosphorus application to enhance spring wheat yield. These results highlighted practical significance of this prediction system, based on neural network utilizing optimal hyperparameters that are ready for practical implementation by system developers.

Optimal configuration of the artificial neural network to estimate the speed of the BLDC motor

Optimal configuration of the artificial neural network to estimate the speed of the BLDC motor

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Adaptive and Contract Aware Configuration for Industrial Wireless Networks

Planning the transmission routing and scheduling is a challenging issue in industrial wireless networks (IWN) due to high quality of service (QoS) requirements. Moreover, in many applications we face the problem of node mobility, changes of transmission conditions, node resource and powering limitations. This paper initially formulates the problem of searching optimal network configuration covering the outlined features and subsequently proposes an effective solution. It is designed for typical industrial FDMA and TDMA networks with horizontal communication. The goal of our studies is to find IWN configuration (routing, time slot and channel allocation) that maximizes the number of successfully concluded contracts. The presented method is compatible with the ISA100.11a standard and integrates relevant QoS requirements for different periodic and aperiodic contracts. Our approach is based on original deterministic algorithms for planning the traffic. They are optimized with a genetic algorithm and facilitate the adaptation to changing operation conditions of network nodes. Extensive simulation experiments have been used in the development and evaluation of the proposed algorithms. The introduced test environment and benchmarks confirmed the advantages of our approach and revealed drawbacks of other approaches.

Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany

Well-designed monitoring networks are crucial for obtaining precise locations, magnitudes and source parameters, both for natural and induced microearthqakes. The performance of a seismic network depends on many factors, including network geometry, signal-to-noise ratio (SNR) at the seismic station, instrumentation and sampling rate. Therefore, designing a high-quality monitoring network in an urban environment is challenging due to the high level of anthropogenic noise and dense building infrastructure, which can impose geometrical limitations and elevated construction costs for sensor siting. To address these challenges, we apply a numerical optimization approach to design a microseismic surveillance network for induced earthquakes in the metropolitan area of Munich (Germany), where several geothermal plants exploit a deep hydrothermal reservoir. First of all, we develop a detailed noise model for the city of Munich, to capture the heterogeneous noise conditions. Then, we calculate the expected location precision for a randomly chosen network geometry from the body-wave amplitudes and travel times of a synthetic earthquake catalog considering the modeled local noise level at each network station. In the next step, to find the optimum network configuration, we use a simulated annealing approach in order to minimize the error ellipsoid volume of the linearized earthquake location problem. The results indicate that a surface station network cannot reach the required location precision (0.5 km in epicentre and 2 km in source depth) and detection capability (magnitude of completeness Mc = 1.0) due to the city´s high seismic noise level. In order to reach this goal, borehole stations need to be added to increase the SNR of the microearthquake recordings, the accuracy of their body-wave arrival times and source parameters. The findings help to better quantify the seismic monitoring requirements for a save operation of deep geothermal projects in urban areas.

Optimal artificial neural network configurations for hourly solar irradiation estimation

&lt;span lang="EN-US"&gt;Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.&lt;/span&gt;

Enhancing gas pipeline network efficiency through VIKOR method

The optimization of gas pipeline networks is critical for efficient and cost-effective transportation of natural gas. This study develops a mathematical model capable of analyzing different network configurations, including branched and branched-cyclic topologies, to explore the optimization of gas pipeline network conditions. The research provides valuable insights into the gas pipeline network optimization process, empowering industry stakeholders to make informed decisions and enhance performance in terms of efficiency, reliability, and cost-effectiveness. To attain these objectives, this study utilizes advanced simulation tools, state-of-the-art optimization algorithms, and sophisticated mathematical models that accurately represent the network's behavior. The optimization process aims to minimize the network's power requirements while simultaneously maximizing gas flow rate and optimizing line pack, ensuring optimal utilization of the pipeline infrastructure. The VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is identifying the most optimal network configuration and operating conditions. Our analysis applies this approach to three case studies, demonstrating its effectiveness in identifying the best network configurations. Additionally, the calculations of total cost and fuel consumption coincide with relative closeness, which confirms the accuracy of our proposed method, whereas optimal scenarios of the three cases have the minimum total cost among all scenarios. In conclusion, this research successfully develops a mathematical model and optimization approach to tackle the complexities of gas pipeline network optimization. The application of The VIKOR method and the analysis of case studies offer substantial evidence of its effectiveness.

Using a Convolutional Neural Network for Machine Written Character Recognition

Convolutional neural networks are special types of artificial neural networks that can solve various tasks in computer vision, such as image classification, object detection, and general recognition. The paper presents the basic building blocks of convolutional neural networks and their architecture, and compares their recognition accuracy with other character recognition techniques using the example of character recognition from vehicle registration plates. The purpose of the experiments was to determine the optimal configuration of the convolutional neural network and the influence of the size and design method of the training set on the recognition rate. The study shows that although convolutional neural networks have recently gained attention, traditional recognition methods are still relevant, and the choice of the right classifier and its configuration depends on the type of recognition task.

An incentivized scheme for electric vehicle charging demand management

The market share of electric vehicles (EVs) is expected to grow, necessitating infrastructure development to support this expansion. However, scientific literature predominantly focuses on charging infrastructure design, overlooking the importance of pricing strategies that can optimize their utilization. To address this gap, this study presents an integrated incentive scheme aimed at maximizing system utilization by effectively distributing the charging demand across stations. Moreover, a novel concept of Flex Accessible Charging Points (FLEX-ACP) is introduced, specifically designed to enhance accessibility and reduce waiting time for users with disabilities and seniors (UDS). The proposed framework utilizes an optimal charging network configuration, considering the number and locations of charging facilities, and then determines appropriate incentives at each station to minimize total waiting time. The problem is formulated as a bi-level optimization model, which is subsequently transformed into a single-level mixed-integer nonlinear program (MINLP). To solve this program, a combination of heuristic and branch-and-cut algorithms is employed. The developed models are tested through a realistic case study, utilizing real-world data from Salt Lake County in Utah.

MiFi: Bounded Update to Optimize Network Performance in Software-Defined Data Centers

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.

Coherence resonance and stochastic synchronization in a small-world neural network: an interplay in the presence of spike-timing-dependent plasticity

Coherence resonance (CR), stochastic synchronization (SS), and spike-timing-dependent plasticity (STDP) are ubiquitous dynamical processes in biological neural networks. Whether there exists an optimal network and STDP configuration at which CR and SS are both pronounced is a fundamental question of interest that is still elusive. We expect such a configuration to enable the brain to make synergistic and optimal use of these phenomena to process information efficiently. This paper considers a small-world network of excitable Hodgkin–Huxley neurons driven by channel noise and STDP with an asymmetric Hebbian time window. Numerical results indicate specific network topology and STDP parameter intervals in which CR and SS can be simultaneously enhanced. Our results imply that an optimally tuned inherent background noise, STDP rule, and network topology can play a constructive role in enhancing both the time precision of firing and the synchronization in neural systems.

A comparison of potential healing agents for vascular-based self-healing concrete

Vascular self-healing concrete is an innovative technology that can potentially improve the durability and longevity of concrete structures. However, limited research is available concerning this type of self-healing compared to intrinsic or capsule-based healing. As the rheology and curing properties of a healing agent can dictate the optimal design configuration of a vascular network, a series of testing procedures for evaluating healing agents is further explored. In this study, the suitability of various commercially available healing agents is considered using a vascular network system in mechanical loading and water absorption test set-ups. In this particular configuration, high sealing efficiencies were obtained for most of the healing agents used, and the polyurethanes and epoxy resin that were studied showed high load regain values. This work provides a testing methodology to select a healing agent in terms of its mechanical load regain, sealing efficiency, rheology, and curing properties, and can be used to determine a suitable healing agent for vascular healing applications.

Development of an efficient ammonia-water power cycle through heat exchanger network analysis and artificial neural network

• The heat integration of the reference Kalina cycle is studied. • Increasing the internal heat recovery can improve the thermal performance. • The improvement in COP can be as high as 8.5%. • A thermal relation embedded artificial neural network is proposed. • The generation performance can be increased by at least 25% at small training sets. This paper presents an efficient ammonia-water power cycle based on the basic Kalina cycle. The heat exchanger network analysis (HEN) is applied to identify all the possible heat-saving configurations. There are 13 different cycles proposed, and each is analyzed and optimized under the same working condition. The results show that there exists a saturation region between the cycle complexity and thermal performance. The thermal efficiency of the optimum cycle has been increased by 8.5% compared to that of the reference Kalina cycle under the heat source temperature of 550℃ and cooling temperature of 24.5℃. The artificial neural network (ANN) surrogate model is also provided to alternatively determine the maximum thermal efficiency under the condition that there is sufficient training data available. The results are compared with those obtained from the thermal model within 0.06% to 1.2% differences. The common practices of ANN in the thermal systems are treated as black-box. Ignorance of thermal principles can lead to a misleading result when using the black-box model, especially for the small size of the training set. In this paper, a thermally constrained ANN architecture is proposed by considering the first law of thermodynamics. The results indicate that, as compared to the black-box model, the generalization performance can be increased at least by 25% with the optimum network configuration of 15-6-3-3 for 50 instances in the training set.

Energy-efficient Data Aggregation in Low-power Wireless Networks with Sensors of Discrete Transmission Ranges: A Mathematical Framework for Network Design

The advancements in sensor technology and the evolution of new generation technologies such as the Internet of Things have led to extensive deployment of low-power wireless sensor networks for various surveillance and monitoring applications. The volume and velocity of the data generated by a large number of sensors and monitors used in such applications are huge. The harnessing of such big data is critical to the real-time control of underlying real-world processes. Future generation networks favor the deployment of sensors with different discrete transmission ranges. Such multiple transmission ranges introduce different connectivity constraints in the network. Advanced strategies are under investigation for designing energy-efficient data aggregation schemes in such connectivity-constrained networks. To aid such works, we introduce a mathematical framework that captures the salient features of low-power networks with sensors of multiple transmission ranges. The proposed framework can serve as a baseline platform to investigate different networking problems in such systems. Further, we consider a cluster-based multihop network with fixed intra-cluster and inter-cluster transmission ranges and address the problem of designing an optimal network configuration for minimizing the data transmission in the network. We model the problem as an integer linear program, which can be applied only to networks of small sizes because of the hardness of the problem. To address the problem in large-scale networks, we design a polynomial-time approximation method using the proposed mathematical framework. The performance of the proposed methods is evaluated under compression schemes based on compressive sensing.

A Novel Radiality Maintenance Algorithm for the Metaheuristic Based Co-Optimization of Network Reconfiguration With Battery Storage

Network reconfiguration (NR) is a well-accepted technique to decrease power losses and enhance the voltage profile of the distribution network. The practical challenges in solving the NP-hard NR problem using metaheuristics are to randomly generate and, in each iteration, check and repair non-radial distribution network configurations without compromising on solution space in the least amount of time. Inefficient handling of non-radial configurations results in large computational time and high standard deviation. This paper mitigates the aforementioned challenges by proposing a novel radiality maintenance algorithm (RMA) that involves the novel concept of junction nodes and a selection set to produce only radial configurations. The proposed approach can potentially improve the standard deviation and computational efficiency of metaheuristics in solving the NR problem. The proposed RMA is generic, model-independent, and scalable, as it can be seamlessly integrated into any metaheuristic approach to solve the NR problem involving feeders of different sizes. The proposed RMA, combined with the accelerated particle swarm optimization, is implemented to solve: 1) the standard snapshot NR problem; and 2) the multiperiod co-optimization problem that simultaneously computes optimal network configuration and control setpoints of the photovoltaic system and battery energy storage system. Simulation results suggest a 27.3% standard deviation reduction in achieving the best results reported in the literature on NR-based power loss reduction within a comparable timeframe of 1.6 seconds. The effectiveness and reliability of the proposed algorithm are demonstrated on IEEE 33-bus test system.