Network Location Research Articles

Measuring the accessibility of metro stations in Tianjin: an origin-destination approach

ABSTRACT The achievement of good spatial accessibility is one of the supreme goals for metro transit planners. While many studies have measured either the accessibility of metro networks or walk accessibility of metro stations, the literature provides limited knowledge for metro station accessibility evaluations integrating both in the context of the origin-destination pair. The present study fills this gap through the use of a set of integrated measures that consider both by-metro accessibility and to-metro accessibility in combination and explores accessibility-based typology among stations. The space syntax is used to conduct the measure models for each dimension (by-metro and to-metro), and typology among stations is divided by an SOM (self-organizing map). The new approach is applied to the case of Tianjin, China. We find that the by-metro accessibility declines from the urban centre to the outskirts of the city and depends mainly on the location in the metro network and the topological depth from the transfer stations. However, the to-metro accessibility varies widely and depends on the street network structure in the station catchment area. Six typologies are identified among stations based on the different characteristics of by-metro accessibility and to-metro accessibility.

How to select the optimal monitoring locations for an aerobiological network: A case of study in central northwest of Spain

Airborne pollen concentration varies depending on several factors, such as local plant biodiversity, geography and climatology. These particles are involved in triggering pollinosis in a share of worldwide human population, and adequate monitoring is, therefore, important. However, the pollen traps in aerobiological monitoring networks are usually installed in cities, and the features of the whole territory are not taken into account. The aim of this study was to analyze what environmental parameters are more suitable as regards setting up monitoring stations throughout a territory in order to obtain an aerobiological network that can represent environmental diversity. The analysis was carried out in 13 locations in Castilla y León over an 8 year period. This is a favorable territory in which to conduct this type of study owing to its climatic features, orography and biodiversity. The ten most abundant pollen types in the region were analyzed, and a clustering analysis was calculated with different distances so as to obtain homogeneous groups of stations. Moreover, the clusters obtained were analyzed in combination with altitudinal and different bioclimatic parameters, which derived from temperature and precipitation.The result here shows that the Castilla y León aerobiological network RACYL represents most of the environmental variability of the territory. Furthermore, it can be divided into two clusters and five sub-clusters for which the start of the main pollen season is different. This corresponds with the division of the territory as regards bioclimatic conditions. The most important bioclimatic parameters were the seasonality of the precipitation and the maximum temperature of the warmest month, although orography must also be taken into account. All of these help discover the optimal places in which to install traps and could reduce the number of monitoring stations. This study additionally provides data for unmonitored areas with similar bioclimatic conditions to those monitored.

Research on Fault Location of Distribution Network with Distributed Generation

Research on Fault Location of Distribution Network with Distributed Generation

Estimate the limit of predictability in short-term traffic forecasting: An entropy-based approach

Accurate short-term traffic forecasting is the cornerstone for Intelligent Transportation Systems. In the past several decades, many models have been proposed to continuously improve the predictive accuracy. A key but unsolved question is whether there is a theoretical bound to the accuracy with which traffic can be predicted and whether that limit can be directly estimated from data. To answer this question, we use core concepts in information theory to derive the limit of predictability in short-term traffic forecasting. Theoretical analysis proves that conditional differential entropy poses a rigorous lower bound of negative-log-likelihood (NLL) for probabilistic models. And the continuous form of Fano’s theorem further gives a loose lower bound of mean-square-error (MSE) for deterministic models. Based on the special properties of traffic dynamics, two assumptions are made in the estimate of entropy metrics: cyclostationarity (traffic phenomena show strong periodicity) and localized spatial correlation (due to kinematic wave propagation). They allow formulating the limit of predictability as a function of longitudinal space and time-of-day which finds the most uncertain locations and periods solely from data. Experiments on univariate traffic accumulation forecasting and network-level speed forecasting show that the selected models, including some state-of-the-art deep learning models, indeed cannot outperform the estimated lower bounds but just approach them. The limit of predictability depends on time-of-day, network locations, observation range, and prediction horizon. The results reveal that the stochastic nature of traffic dynamics and improper assumptions on the prior distribution of output are two major factors that restrict the predictive performance. In summary, the proposed method estimates a trustworthy performance boundary for most traffic forecasting models. These conclusions are helpful for further studies in this domain.

Spatializing Social Networking Analysis to Capture Local Innovation Flows towards Inclusive Transition

The location of the local network of firms impacts, positively or negatively, their economic performance. The interactions between different sectors in a territory are still not easily observable. We test the complexity of the economic structure at a local level, given the availability of data at a very granular scale. This could greatly assist in observing sectors or/and locations that play a dominant role in the regional economy. Thus, in order to interpret the economic structure of a territory, we used cluster-based analysis. The analysis helps in evaluating the interconnections among sectors that constitute a cluster. A novel method of describing the territorial economic structure is presented by applying Social Network Analysis (SNA) within cluster-based analysis to characterize the importance of both location and economic interconnections. In this study, we focus on the industrial agglomerations in Calabria, Italy, to underpin the potential of the region’s industries by using social networking analysis metrics. This research put forward new interpretations of SNA metrics that describe regional economic compositions. Our findings reveal that territorial social networks are a potential instrument for understanding interactions in regional systems and economic clusters and might help in highlighting local industrial potentials. We believe that this study’s results could be considered as the initial steps for a pioneer data-driven place-based structural analysis model.

Supply chain insights from social media users’ responses to panic buying during COVID-19: the herd mentality

PurposeThis study applied the herd mentality theory to explore local and global social media users’ responses to panic buying across the USA, UK and Australia during the COVID-19 crisis to understand the implications on operations and supply chains.Design/methodology/approachA total of 208,806 social media user-generated content (UGC) pieces were collected from Twitter in three countries – the USA, UK and Australia. The analysis of this big qualitative data was performed using machine learning–based software – Leximancer.FindingsPositive and negative sentiment towards panic buying during the COVID-19 crisis was observed in the UGC. No significant differences in social media UGC sentiment between the three countries were found; however, differences did exist in key themes. This suggests that the focus, not the sentiment, of consumers’ responses to panic buying differed across countries. Social media users follow their location-based and topic-consonant social “herd”, rather than the global “herd”.Research limitations/implicationsThis study was the first to show that social media users’ herd mentality differs in a crisis. The herd mentality of social networks is dependent on factors such as the geographic location of the social network (herd), which can differ from the global herd’s reaction, specifically in terms of topics evident in UGC.Practical implicationsOperations and supply chain managers need to include social media UGC analysis in their strategies in crisis management responses. The topics, not the sentiment, of consumers’ responses to panic buying require managerial actions.Originality/valueThis is the first study to show that herd mentality during a crisis, such as COVID-19, is not unidimensional and varies according to the location of the social media network with profound implications for operations and supply chain managers.

Fault Location Analysis in Active Distribution Network Based on Multi-population Genetic Algorithm

Because the fault location of active distribution network is a complex optimization model, the traditional genetic algorithm has the problems of premature and slow convergence when solving complex optimization problems. The switch function that can adapt to the switching of multiple distributed power sources in real time is proposed. Here, we proposed a novel method to locate the fault of distributed power distribution network based on a multi-group genetic algorithm. When locating the fault zone, The algorithm stipulates that the direction of the generator flowing to the electrical equipment is the forward direction of the transmission line. In this paper, multiple populations are used to search the solution range at the same time to ensure that the final solution is the global optimal solution, and the optimal individual retention algebra is used as the convergence condition to fully improve the solution speed, which is suitable for complex distribution networks with distributed power generation. The fault location model proposed in this paper is simulated by a numerical example, and the results show that the model can accurately find the fault point, and has validity and accuracy.

Wireless Position Method for Indoor Personnel Based on Improved Neural Network by Chaotic Whale Optimization Algorithm

The positioning accuracy of the indoor personnel positioning system is low, which cannot meet the needs of an intelligent transformer substation. Therefore, a wireless positioning method for indoor personnel based on the Elman neural network (ENN) using chaotic whale optimization algorithm (CWOA) was proposed. Firstly, a certain number of sample fingerprint databases were collected by wireless terminals in the wireless network on indoor personnel. Secondly, CWOA was used to optimize the weights and self-connected feedback gain factor of the ENN. Thirdly, the fingerprint database was used to train and test the optimized ENN, and the position algorithm model of the neural network was established. Finally, the real-time position was achieved with the neural network location algorithm model through the fingerprint data of the position point collected by the wireless terminal. The results show that the average position error of this indoor wireless positioning method is 1.24 m; ENN position algorithm by the CWOA has a stronger global search ability and is more suitable for the application in the time-varying indoor environment.

Improved binary gaining–sharing knowledge-based algorithm with mutation for fault section location in distribution networks

AbstractFault section location (FSL) plays a critical role in shortening blackout time and restoring power supply for distribution networks. This paper converts the FSL task into a binary optimization problem using the feeder terminal unit (FTU) information. The discrepancy between the reported overcurrent alarms and the expected overcurrent states of the FTUs is adopted as the objective function. It is a typical 0–1 combinatorial optimization problem with many local optima. An improved binary gaining–sharing knowledge-based algorithm (IBGSK) with mutation is proposed to effectively solve this challenging binary optimization problem. Since the original GSK cannot be applied in binary search space directly, and it is easy to get stuck in local optima, IBGSK encodes the individuals as binary vectors instead of real vectors. Moreover, an improved junior gaining and sharing phase and an improved senior gaining and sharing phase are designed to update individuals directly in binary search space. Furthermore, a binary mutation operator is presented and integrated into IBGSK to enhance its global search ability. The proposed algorithm is applied to two test systems, i.e. the IEEE 33-bus distribution network and the USA PG&E 69-bus distribution network. Simulation results indicate that IBGSK outperforms the other 12 advanced algorithms and the original GSK in solution quality, robustness, convergence speed, and statistics. It equilibrates the global search ability and the local search ability effectively. It can diagnose different fault scenarios with 100% and 99% success rates for these two test systems, respectively. Besides, the effect of mutation probability on IBGSK is also investigated, and the result suggests a moderate value. Overall, simulation results demonstrate that IBGSK shows highly promising potential for the FSL problem of distribution networks.

A Non-parametric Bayesian Network for multivariate probabilistic modelling of Weigh-in-Motion System Data

Weigh-in-motion (WIM) systems help to collect data such as vehicular loads, individual axle loads, vehicle type, and number of axles. This is relevant in engineering because traffic load performs an essential function in the design of new bridges and in the reliability assessment of existing ones, in traffic analysis and other areas of engineering. Therefore, when WIM data is not available, computing synthetic WIM observations that adequately approximate the statistical dependence between variables is important. In this paper, WIM measurements from the Netherlands and Brazil were analysed, and a set of non-parametric Bayesian Networks (NPBNs) is presented. This paper significantly improves on previous results by allowing observations of inter-axial distance to be generated, by allowing several sources of data to be used in the modelling and by making software available to researchers and practitioners interested for generating synthetic observations based on the distribution of vehicle type. In particular, statistical models to describe the weight and length of different vehicle types are derived. Three NPBNs were quantified using data from: (i) six WIM locations of the motorway network of the Netherlands, (ii) one WIM location in one city route of Rotterdam, The Netherlands, and (iii) one WIM location of one highway in Araranguá city located in the south of Brazil. Additionally, a Graphical User Interface (GUI) for the six Dutch WIM motorways locations was developed. To illustrate a possible use of the model when WIM data is not available. The GUI was used to compute synthetic WIM observations using data collected through traffic counters gathered in Toluca city in central Mexico, as input. This paper shows that the methodology here presented is widely applicable and depends only on the assessment of vehicle type configuration.

Correction to: Can high-performance funds be built and managed by improving their network locations? –- evidence from entrepreneurship in Chinese fund managers

Correction to: Can high-performance funds be built and managed by improving their network locations? –- evidence from entrepreneurship in Chinese fund managers

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Authenticating source information of distribution synchrophasors at intra-state locations for cyber-physical resilient power networks

As power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information and communication technologies, they become increasingly reliant upon more accurate monitoring and fast control. Phasor Measurement Units (PMUs) collect high-precision Distribution Synchrophasors (DS) data regarding the system dynamics and provide real-time situational awareness for better monitoring and control of large-scale power grids. The accuracy and generalizability of the PMUs heavily rely upon the data quality of DS measurements, which is very susceptible to newly emerging “Source ID Mix” data spoofing attacks. Such attacks could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby jeopardizing most DS-based applications and even causing catastrophic power interruptions. This paper proposes a novel data-driven source authentication method to automatically identify the source information of DS collected from multiple intra-state locations and thus enhance the reliability and cybersecurity of power systems. The proposed method integrates Mathematical Morphological Decomposition (MMD) and Multi-Weighted Deep Stacking Forest (MLW-DSF) for providing accurate source authentication with a low computational cost which requires neither system’s models nor parameters. The effectiveness of the proposed method is corroborated through seven “state-of-the-art” data-driven models by using real-life DS measurements of power networks in Queensland state.© 2017 Elsevier Inc. All rights reserved.

Optimization of Reclosers Placement in Distribution Networks to Improve Service Quality Indices

The fundamental goal of an electric utility is to provide the power supply in the most reliable and economical way. However, the components of a power distribution system are exposed to faults, or service outages, which in some cases may cause the disconnection of one or more customers of the distribution system. In order to reduce the duration of faults and improve service quality indices in power networks, utilities have installed protection devices, such as reclosers, in appropriate locations in the power distribution networks, due to its ability of coordination with other device and the radial characteristics of distribution system. In addition, the recloser can clear temporary faults and isolate failed feeders without interrupting the service of the whole circuit. This paper presents a methodology for optimization of reclosers placement in distribution networks. A mathematical model of Mixed Integer Nonlinear Programming (MINLP) is used, with the purpose to minimize the SAIDI or SAIFI quality indices. For the model, an objective function is defined, with its respective restrictions to obtain the appropriate locations. This objective function is run in the GAMS software to model and solve optimization problems. The effectiveness of the proposed approach is evaluated on a test system and in a conventional real distribution system.

Fault Location and Classification for MVDC Networks

This paper proposes a two step fault detection scheme for medium-voltage dc (MVDC) networks based on wavelet analysis. The proposed Grid Transients Classifier (GTC) is used to continuously monitor energy of wavelet packet decomposition levels (WPDL) for voltage and current signals to determine any transient disturbance like load change, pole to pole (PP) or pole to ground (PG) faults, pole to pole to ground (PPG), voltage sags, switching, etc. Subsequently, type of disturbance is identified through an artificial neural network (ANN) classifier. Then, proposed Active Grid Impedance Estimator (AGIE) injects a signal with two fundamental frequencies to evaluate grid impedance from the measurement point in new condition. Using the obtained data, fault type, location and its severity are specified. Hardware-in-the-Loop (HIL) results are provided for a shipboard grid case study to validate the real-time application of the proposed method and demonstrate its effectiveness.

An interdisciplinary agent-based multimodal wildfire evacuation model: Critical decisions and life safety

This article applies an interdisciplinary agent-based model (ABM) of wildfire evacuation to the devastating 2018 wildfire in Mati, Greece, where the second-deadliest wildfire of the 21st century took place. This model integrates the natural hazard system (wildfire propagation) with the sociotechnical response system comprising social (population response) and engineered (transportation network and shelter location) components. The research objective is to investigate the effects on wildfire casualties of the risk area population’s decisions about (1) whether to leave and how long to wait (i.e., departure time); (2) what transportation mode to use (e.g., walking or driving); and (3) how fast to travel. Analysis of several evacuation scenarios shows that the absence of children, multi-modal travel, staged evacuation, and increased shelter capacity lead to a more successful wildfire evacuation. These analyses can help emergency managers improve the effectiveness of their communities’ wildfire evacuation plans.

Securing Hybrid Architecture of Cloudlet Computing in 5G Networks Enabling IoT and Mobile Wireless Devices

Aims: This research aims to secure and support mobile devices and IoTs enabled in WLAN and 5G Multi-Access Edge Computing (MEC) infrastructures. Background: Currently, wireless network access gains increasing potential in today's networks. At the same time, such ongoing wireless network access also raises the risk of network attacks. The 5G technology is expected to empower people inter-communications by integrating wireless networking technologies in all kinds of networks. Additionally, the emerging 5G technologies put forward many new requirements for RF characteristics and techniques regarding bandwidth and power issues, in addition to supporting high connectivity capacity for the emerging IP-based IoT applications. Objective: The objective is to provide a hybrid architecture of the proposed framework for a 5G network with cloudlet computing. The purpose that we are focusing on it is to support and monitor wireless networks under attack. Methods: The solution is done through a hybrid architecture. In this architecture, the cloudlet and Wi-Fi access points (AP) are integrated for providing an integrated Wi-Fi-enabled cloudlet. On the other hand, we have specified a solution for detecting unauthorized APs by using authentication and authorization techniques for securing communications among endpoint devices such as IoT devices or mobile devices, cloudlet, and the main cloud. A framework of this architecture has been developed to face the issue of securing IoT devices and mobile wireless devices. Results: The traffic sniffing and traffic filtering of the endpoints are done. Therefore, the required actions that define the organization's policy are applied through the permitted access list. In addition to a wireless network location, the management components use more than one authorized attribute value to generate the authorized list, such as SSID, MAC, BSSID …etc. Finally, Traffic analysis provides the user to get reports, statistics, and analysis charts of secure endpoints activities. Conclusion: We are focusing on one of the critical security issues of wireless networks. Namely, the issue of unauthorized access becomes more critical due to getting access to a network without subscriber permission. Moreover, the proposed solution allows managing access control permissions to allow or block access to resources for the user of a mobile device.

Modeling of electric springs and their multi-objective voltage control based on continuous genetic algorithm for unbalanced distribution networks

This work proposes a multi-objective voltage control for multiple Electric Springs (ESs) in active unbalanced distribution systems, where the goal is to provide a suitable simulation scheme to control the ESs in real-time applications. This multi-objective voltage control relies on a robust ES model, a modified backward/forward solution method (BFSM), and a continuous genetic algorithm (CGA). The proposed ES model is suitable for unbalanced conditions and can be implemented at any network location in single-, two-, and three-phase connections. Also, it considers the self and mutual effects of the distribution line (DL) and the power flow of the elements connected at the point of common coupling (PCC) to determine the ES output voltage. The ES model is adapted to the BFSM to quickly and reliably solve the power flow in the distribution network. Besides, the CGA simultaneously solves the mode of operation and the amount of reactive power exchange of the ESs to minimize power losses, voltage unbalance, and voltage deviation. The proposed voltage control is evaluated on an active distribution network based on a modified IEEE 13-bus for considering both load variations and power intermittences.

WSN Node Based on Adaptive Cuckoo Search Algorithm for Agricultural Broadcast Positioning

The node location of wireless sensor network (WSN) is actually a multi-dimensional constraint optimization problem for measuring distance and range error. A new adaptive cuckoo search algorithm is proposed to solve the problems of the standard cuckoo search algorithm, such as slow convergence rate and easy to get into local optimum. Firstly, the algorithm has a large searching space in the early stage and improves the global searching ability by adjusting the flight step length of Levy. Secondly, dynamic inertial weight and memory strategy are introduced for random swimming; therefore the algorithm can make full use of historical experience and improve the stability. Finally, simulation results show that the proposed algorithm can effectively improve the positioning accuracy without increasing the hardware cost.

Machine Learning-Based Fault Location for Smart Distribution Networks Equipped with Micro-PMU.

Faults in distribution networks occur unpredictably, causing a threat to public safety and resulting in power outages. Automated, efficient, and precise detection of faulty sections could be a major element in immediately restoring networks and avoiding further financial losses. Distributed generations (DGs) are used in smart distribution networks and have varied current levels and internal impedances. However, fault characteristics are completely unknown because of their stochastic nature. Therefore, in these circumstances, locating the fault might be difficult. However, as technology advances, micro-phasor measurement units (micro-PMU) are becoming more extensively employed in smart distribution networks, and might be a useful tool for reducing protection uncertainties. In this paper, a new machine learning-based fault location method is proposed for use regardless of fault characteristics and DG performance using recorded data of micro-PMUs during a fault. This method only uses the recorded voltage at the sub-station and DGs. The frequency component of the voltage signals is selected as a feature vector. The neighborhood component feature selection (NCFS) algorithm is utilized to extract more informative features and lower the feature vector dimension. A support vector machine (SVM) classifier is then applied to the decreased dimension training data. The simulations of various fault types are performed on the 11-node IEEE standard feeder equipped with three DGs. Results reveal that the accuracy of the proposed fault section identification algorithm is notable.