Operational Network Research Articles

Ground-based laser momentum transfer concept for debris collision avoidance

Satellite laser ranging (SLR) is a well-established technology within the scientific community used since the early 1960s to precisely measure distances. The technology evolved in support of the geodetic research striving for high accuracy measurements which nowadays are achieved by means of high repetition, low energy pulse lasers used in combination with satellites equipped with retroreflectors. The achieved accuracy allowed not only for quality improvement of orbit determination products but also remote estimation of the attitude of the observed target.The SLR technique constitutes a highly accurate, relatively cheap alternative to radars for the tracking of orbiting targets. In the last decade, the successful tracking of resident space objects, not equipped with retroreflectors, made SLR a fundamental and appealing technique also in the space debris domain.In this study, we will introduce a possible step forward - thanks to the availability of commercial high power (> 10 kW) continuous wave (CW) lasers - which consists in the setup of a network of ground stations able to efficiently contribute to space debris collision avoidance manoeuvres in the low Earth orbit (LEO).This paper will summarize the achievements of a conceptual study on ground-based laser momentum transfer to LEO space debris performed by a consortium under the guidance of the German Aerospace Centre (DLR) funded by the European Space Agency (ESA) in the frame of the ESA Space Safety Programme.The study was carried out approaching the problem from an astrodynamics, physical, technological and legal point of view. The required tracking precision and the fundamental physics of the laser momentum transfer (LMT) were studied to evaluate the achievable thrust on LEO debris objects with commercially available components. An astrodynamics analysis was carried out to assess the efficiency of the imposed thrust and the consequences on the probability of collision in LEO. In the paper we will report the outcomes of the study which allowed us to define: the requirements of a laser tracking and momentum transfer (LTMT) station, the minimum size of an LTMT network for LEO collision avoidance operation, the current technological challenges, and gaps to be filled before its implementation.

Bi-objective design-for-control for improving the pressure management and resilience of water distribution networks

This paper investigates control and design-for-control strategies to improve the resilience of sectorized water distribution networks (WDN), while minimizing pressure induced pipe stress and leakage. Both evolutionary algorithms (EA) and gradient-based mathematical optimization approaches are investigated for the solution of the resulting large-scale non-linear (NLP) and bi-objective mixed-integer non-linear programs (BOMINLP). While EAs have been successfully applied to solve discrete network design problems for large-scale WDNs, gradient-based mathematical optimization methods are more computationally efficient when dealing with large search spaces associated with continuous variables in optimal network control problems. Considering the advantages of each method, we propose a sequential hybrid method for the optimal design-for-control of large-scale WDNs, where a refinement stage relying on gradient-based mathematical optimization is used to solve continuous optimal control problems corresponding to design solutions returned by an initial EA search. The proposed method is applied to compute the Pareto front of a bi-objective design-for-control problem for the operational network BWPnet, where we consider reopening closed connections between isolated supply areas. The results show that the considered design-for-control strategy increases the resilience of BWPnet while minimizing pressure induced leakage. Moreover, the refinement stage of the proposed hybrid method efficiently improves the coarse approximation computed by the initial EA search, returning a continuous and even Pareto front approximation.

2D Self-organized ONN model for Handwritten Text Recognition

Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs’ learning performance is limited since they are homogeneous networks with a simple (linear) neuron model. With their heterogeneous network structure incorporating non-linear neurons, Operational Neural Networks (ONNs) have recently been proposed to address this drawback. Self-ONNs are self-organized variations of ONNs with the generative neuron model that can generate any non-linear function using the Taylor approximation. In this study, in order to improve the state-of-the-art performance level in HTR, the 2D Self-organized ONNs (Self-ONNs) in the core of a novel network model are proposed. Moreover, deformable convolutions, which have recently been demonstrated to tackle variations in the writing styles better, are utilized in this study. The results over the IAM English dataset and HADARA80P Arabic dataset show that the proposed model with the operational layers of Self-ONNs significantly improves Character Error Rate (CER) and Word Error Rate (WER). Compared with its counterpart CNNs, Self-ONNs reduce CER and WER by 1.2% and 3.4 % in the HADARA80P and 0.199% and 1.244% in the IAM dataset. The results over the benchmark IAM demonstrate that the proposed model with the operational layers of Self-ONNs outperforms recent deep CNN models by a significant margin while the use of Self-ONNs with deformable convolutions demonstrates exceptional results.

Passive detection in water pipelines using ambient noise II: Field experiments

A passive detection method has been proposed in a prior paper to extract key parameters and detect faults using the ambient noise present in water pipeline networks. This paper presents field experiments and data processing results to provide systematic experimental validation of this method. Field experiments were carried out in operational water pipeline networks at the University of Canterbury campus and the Waimakariri District, New Zealand, during which ambient noise was measured by pairs of pressure sensors installed at selected hydrants on pipelines of different materials, network topologies and simulated faults. Auto-correlation and cross-correlation analysis of noise at a single sensor and sensor pairs were carried out to estimate the wave speed and to locate faults in the networks. Data processing results indicate that water usage generating pressure transients are the dominant sources of ambient noise in operational water pipeline networks. This type of ambient noise can also be utilized by the passive detection method to achieve similar wave speed estimation accuracy and fault detection performance as the conventional active pressure wave detection methods.

Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. To meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

Rainfall-induced landslide early warning system based on corrected mesoscale numerical models: an application for the southern Andes

Abstract. Rainfall-induced landslides (RILs) are an issue in the southern Andes nowadays. RILs cause loss of life and damage to critical infrastructure. Rainfall-induced landslide early warning systems (RILEWSs) can reduce and mitigate economic and social damages related to RIL events. The southern Andes do not have an operational-scale RILEWS yet. In this contribution, we present a pre-operational RILEWS based on the Weather and Research Forecast (WRF) model and geomorphological features coupled to logistic models in the southern Andes. The models have been forced using precipitation simulations. We correct the precipitation derived from WRF using 12 weather stations through a bias correction approach. The models were trained using 57 well-characterized RILs and validated by ROC analysis. We show that WRF has strong limitations in representing the spatial variability in the precipitation. Therefore, accurate precipitation needs a bias correction in the study zone. We used accurate precipitation simulation and slope, demonstrating a high predicting capacity (area under the curve, AUC, of 0.80). We conclude that our proposal could be suitable at an operational level under determined conditions. A reliable RIL database and operational weather networks that allow real-time correction of the mesoscale model in the implemented zone are needed. The RILEWSs could become a support to decision-makers during extreme-precipitation events related to climate change in the south of the Andes.

Traffic Refinery

Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the model can be deployed in practice. Thus, the design and evaluation of these models ultimately requires understanding not only model accuracy but also the systems costs associated with deploying the model in an operational network. Towards this goal, this paper develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for two practical network management tasks, video streaming quality inference and malware detection, to demonstrate the importance of exploring different representations to find the appropriate operating point. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept implementation that both monitors network traffic at 10~Gbps and transforms traffic in real time to produce a variety of feature representations for machine learning. Traffic Refinery both highlights this design space and makes it possible to explore different representations for learning, balancing systems costs related to feature extraction and model training against model accuracy.

Detection of supercooled liquid water containing clouds with ceilometers: development and evaluation of deterministic and data-driven retrievals

Abstract. Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.

Implementation of the Personnel Management Information System (SIMPEG) at the Ministry of Religion of North Sumatra Province

Describing the activities of adopting and implementing Information Communication and Technology (ICT) as a tangible manifestation of renewal in the government environment is the purpose of this research. This is also a form of evidence of changes and standardization of each employee, especially related to prime play. One of its manifestations is the North Sumatra Provincial Government's initiative to implement the Personnel Management Information System (SIMPEG) at the Ministry of Religion of North Sumatra Province. This study uses a descriptive qualitative approach by integrating primary and secondary data in order to analyze more deeply the implementation of SIMPEG. Therefore, as a recommendation is the need to increase the capacity of human resources through education and training including operational policies, budgets and network facilities.

Cascade Forward Neural Networks-based Adaptive Model for Real-time Adaptive Learning of Stochastic Signal Power Datasets

In this work, adaptive learning of a monitored real-time stochastic phenomenon over an operational LTE broadband radio network interface is proposed using cascade forward neural network (CFNN) model. The optimal architecture of the model has been implemented computationally in the input and hidden units by means of incremental search process. Particularly, we have applied the proposed adaptive-based cascaded forward neural network model for realistic learning of practical signal data taken from an operational LTE cellular network. The performance of the adaptive learning model is compared with a benchmark feedforward neural network model (FFNN) using a number of measured stochastic SINR datasets obtained over a period of three months at two indoors and outdoors locations of the LTE network. The results showed that proposed CFNN model provided the best adaptive learning performance (0.9310 RMSE; 0.8669 MSE; 0.5210 MAE; 0.9311 R), compared to the benchmark FFNN model (1.0566 RMSE; 1.1164 MSE; 0.5568 MAE; 0.9131 R) in the first studied outdoor location. Similar robust performances were attained for the proposed CFNN model in other locations, thus indicating that it is superior to FFNN model for adaptive learning of real-time stochastic phenomenon.

Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.

Managerial thinking in neonatal care: a qualitative study of place of care decision-making for preterm babies born at 27–31 weeks gestation in England

ObjectivesPreterm babies born between 27 and 31 weeks of gestation in England are usually born and cared for in either a neonatal intensive care unit or a local neonatal unit—with...

Comparative analysis of algorithms for estimating traffic parameters in corporate multiservice communication networks

Adequate management of modern corporate communication networks is possible if many control procedures function in near real time. In this case, the processed network monitoring data must have accurate characteristics sufficient for making objective management decisions. This fully applies to the data monitoring of network traffic parameters, which determines the relevance of the proposed work. The proposed in the paper algorithm for on-line estimation of traffic parameters in corporate multiservice communication networks is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. Its essence is that the estimation of traffic parameters is performed in two stages - in the first stage, we evaluate the forecast values of parameters, and in the second, with the next observations of random sequences, we make adjustments to their values. Traffic parameter values forecasts are constructed in a small-sized sliding window, and the adjustment is implemented on the basis of pseudo-gradient procedures whose parameters are adjusted using a fuzzy control algorithm based on the Takagi-Sugeno method. The proposed algorithm belongs to the class of adaptive algorithms with prior learning. The maximum value of the average relative error of estimation of traffic parameters was less than 8.2%, which is a sufficient value for the implementation of operational network management tasks. At the same time, the actual scientific and technical task is to analyze the comparison of the characteristics of the developed adaptive algorithm with the characteristics of the optimal algorithms, the characteristics of which are the maximum achievable. Translated with www.DeepL.com/Translator (free version). The results of a comparison of the proposed method with the optimal Coleman filtration (OKF) are presented.

Design and Simulation of DFIG based Wind Energy System using AI EMS

Abstract: Renewable energy sources are considered to be an unpredictable resource especially in wind turbines; rapid variation in wind speeds cause power fluctuations leads to interrupt the power system stability during the high-level power operation. To overcome these issue energy storage systems such as Battery storage is added to balance the grid and helps to balance the generated power and the power demand. The battery system is also used to mitigate the power fluctuations and helps to give more power to balance the load requirement. The proposed (EMS) Energy Management System integrates with the Artificial Neural Network for operation of both battery and wind system depending upon the demand need. In the case of the battery management system, the intelligent controllers help to control the State of Charge (SoC) limits of the battery to make sure the required time of charging and discharging. The proposed Artificial Intelligent Controller acts as an Energy Management System and Battery Management System as well as it is used for power balance generation and load also efficiently mitigating the voltage fluctuation during variation in the load. MATLAB is used for the simulation process since it contains the features of Artificial Intelligence which can in turn function as switching device. Keywords: Renewable energy sources, Battery Storage, Energy management system, State of charge, Artificial neural network etc.

Flexible management and decarbonisation of rural networks using multi‐functional battery control

To support the electrification of heat and transport, distribution network operators are looking to adopt network management solutions which can exploit local flexibility to advance their decarbonisation efforts in line with the evolving management requirements of the network. This paper develops a multi-functional battery control strategy that integrates constraint management with carbon-intensity linked control dispatch – carbon control – to support decarbonisation of rural distribution networks in Scotland by displacing last resort backup diesel generation and facilitating low carbon network balancing in the drive towards self-sustaining local distribution networks. The interplay between the different functionalities is considered to understand the challenges, and opportunities, of adopting multi-functional battery storage as an alternative management solution based on an operational distribution network in Scotland. Case studies are presented which consider network constraints at different times of day and year for various generation, demand and carbon intensity profiles to support this investigation. The findings of this work provide for the near-term, realisation of self-sustaining carbon-neutral local distribution networks that are in keeping with both the operational objectives of incumbent network operators, smart local energy systems and also low carbon policy objectives.

Short-term impacts of the filling transition across elevations in intermittent water supply systems

ABSTRACT Intermittent Water Supply (IWS) is characterised as drinking water distribution systems that are supplied with water for less than 24 hours/day on average. The filling transition from non-supply to pressurised supply is a period of hydraulic instability that negatively impacts water quality, deteriorates infrastructure integrity and intensifies social inequalities. Understanding the factors contributing to the severity of impact from filling is vital to develop effective management strategies. This paper investigates the hydraulic phenomena induced during the short-term IWS filling period and how this behaviour can be exacerbated by elevation and other hydraulic system factors, such as velocity and pressure. Controlled laboratory experiments conducted in the UK and field studies performed in operational networks across South Africa are presented. Results demonstrate the crucial role elevation plays in the network response during the filling transition, emphasising the need for greater consideration of elevation in IWS and well-maintained air relief valves in strategic locations.

Operations design for high-velocity intra-city package service

This paper presents a network design problem in the context of intra-city courier service in megacities. The considered setting is inspired by the activities of one of the main courier package companies in China. The problem consists of designing a robust yet cost-effective service network to move packages from their origins to their destinations taking into account the committed service levels. The operational network includes two layers; the first layer consists of the network of riders, transporting packages from (to) customer locations to (from) a series of hubs, while the second layer consists of a fleet of shuttles responsible for the inter-hub transfers. In this study, we focus on shuttle activities and develop optimization technology for the design of shuttle services using novel rate-based models to determine packages as well as vehicle routes. To ensure operational simplicity, we build repeatable vehicle routes taking the form of cycles that are executed throughout the day, taking into account capacity and timing requirements. A computational study using data from a large Chinese package company shows that our technology produces a cost-effective service network design for shuttle schedules with excellent on-time performance.

Automated identification of network anomalies and their causes with interpretable machine learning: The CIAN methodology and TTrees implementation

Leveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. To help in this task we present CIAN (from Causality Inference of Anomalies in Networks), a practical and interpretable ML methodology, which we implement in the form of a software tool named TTrees (from Troubleshooting Trees). We have designed CIAN to automate the identification of the causes of performance anomalies in cellular networks. Our methodology is unsupervised and combines multiple ML algorithms (e.g., decision trees and clustering) and Kolmogorov complexity-inspired data analysis tools that we have developed for this work. CIAN can be used with small volumes of data and is quick at training.Our experiments use diverse data sets obtained from measurements in operational commercial mobile networks. They show that the TTrees implementation of CIAN can automatically identify and accurately classify network anomalies – e.g., cases for which a network low performance is not apparently justified by operational conditions – training with just a few hundreds of data samples. The resulting information hence enables precise troubleshooting actions. In particular, we showcase how TTrees can be flexibly used to monitor the performance of TCP and QUIC protocols when they are adopted to serve mobile users.

Combining rule-based reasoning and meta-path-based link prediction for heterogeneous target operational network reconstruction

Predicting the connections or interactions between nodes in networks remains a fundamental task in network analysis. In target systems, nodes with different types are connected with each other through different relations, as in typical heterogeneous networks. Link prediction can help identify implied information in heterogeneous target operational network (HTON) to boost network reconstruction. In view of the complex network structure of target systems and diversified types of edges alongside the low accuracy and poor interpretability issues of previous methods, we proposed an HTON reconstruction framework (HTONRP) incorporating rule-based reasoning and meta-path-based link prediction. To start with, the link rules are extracted based on operational doctrine, military theory, system formulation, etc., and then they are reasoned with initial nodes and relations (input) and the inferred target relations (output) to improve the target systems architecture. Afterwards, the data are input into the meta-path-based link prediction model of HTON, through which the architecture of target systems is further perfected. The experiments are carried out on real data instances of HTON and results suggest that the proposed method can effectively improve the prediction performance and enhance model interpretability compared to traditional baselines.

Identification of an interactome network between lncRNAs and miRNAs in thyroid cancer reveals SPTY2D1-AS1 as a new tumor suppressor

Thyroid cancer is the most common primary endocrine malignancy in adults and its incidence is rapidly increasing. Long non-coding RNAs (lncRNAs), generally defined as RNA molecules longer than 200 nucleotides with no protein-encoding capacity, are highly tissue-specific molecules that serve important roles in gene regulation through a variety of different mechanisms, including acting as competing endogenous RNAs (ceRNAs) that ‘sponge’ microRNAs (miRNAs). In the present study, using an integrated approach through RNA-sequencing of paired thyroid tumor and non-tumor samples, we have identified an interactome network between lncRNAs and miRNAs and examined the functional consequences in vitro and in vivo of one of such interactions. We have identified a likely operative post-transcriptional regulatory network in which the downregulated lncRNA, SPTY2D1-AS1, is predicted to target the most abundant and upregulated miRNAs in thyroid cancer, particularly miR-221, a well-known oncomiRNA in cancer. Indeed, SPTY2D1-AS1 functions as a potent tumor suppressor in vitro and in vivo, it is downregulated in the most advanced stages of human thyroid cancer, and it seems to block the processing of the primary form of miR-221. Overall, our results link SPTY2D1-AS1 to thyroid cancer progression and highlight the potential use of this lncRNA as a therapeutic target of thyroid cancer.