Search

In the last few years, Image Enhancement is a very necessary task in computer vision and to enhance the image. Super Resolution (SR) technique has been used to enhance the resolution of image. Super Resolution is a technique which is used to convert High Resolution (HR) from Low Resolution Image (LR). In this paper, to understand SR problems and to design an efficient and robust model, Convolution neural network (CNN) is used. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on other datasets and acheived advance results as compared to existing works.

By incorporating the redefined error monitor function into network design, an error redefinition neural network (ERNN) is proposed to control mobile redundant manipulators to execute the tracking task in this paper. The global asymptotic stability and the strong anti-disturbance capability of the ERNN are proved theoretically. Furthermore, the ERNN can overcome the overshoot and constant disturbance. Meanwhile, the ERNN is input-to-state stable while the bounded time-varying disturbance is considered as the control input.

We study an integrated airline schedule design and fleet assignment model for constructing schedules by simultaneously selecting from a pool of optional flights and assigning fleet types to these scheduled flights. This is a crucial tactical decision which greatly influences airline profit. As passenger demand is often substitutable among available fare products between the same origin-destination pair, we study an optimization model that integrates within it a passenger choice model for fare product selections. To tackle the formidable computational challenge of solving this large-scale network design problem, we propose a decomposition approach based on partitioning the flight network into smaller subnetworks by exploiting weak dependency in the network structure. The decomposition relies on a series of approximation analyses and a novel fare split problem to reliably measure the approximation quality introduced by the partition by optimally allocating fares of products shared by flights in different subnetworks. We present several reformulations by representing fleet assignment and schedule decisions using composite variables and formally characterize the relationship of their strengths. This gives rise to a new reformulation that is able to flexibly trade off strength and size. We conduct detailed computational experiments using two realistically sized airline instances to demonstrate the effectiveness of our approach. Under a simulated passenger booking environment with both perfect and imperfect forecasts, we show that the fleeting and scheduling decisions informed by our approach deliver significant and robust profit improvement over benchmark implementations and previous models in the literature.

The increasing importance of integrated fulfillment concepts revitalizes bricks-and-mortar stores and puts them at the center of retail operations. So-called omnichannel (OC) concepts leverage stores to offer seamless and enhanced operations for offline and online shoppers. Stores are used to fulfill online orders, offer shorter lead times to customers, and extend the assortment across channels. The role of the store and the underlying store operations are thus impacted by profound changes. This transformation has not yet been assessed comprehensively from a practical or an Operations Research (OR) lens. This paper identifies cross-cutting store-related planning issues and develops a planning framework for OC operations. We apply industry interviews and a systematic literature analysis to derive five planning issues. Research gaps are revealed by matching the pertinent OR literature with managerial needs. The planning issues network design of fulfillment locations, assignment of customer orders, and assortment and inventory planning have been discussed in several store-related OC publications. Demand forecasting and inventory replenishment have received less coverage, and offer significant research opportunities.

Mobile operators collect and store the network generated traffic data for analysis. Time Series Prediction (TSP) has been used in mobile network traffic data analysis to produce predictive results for network planning and resource allocation. We propose a novel method of predicting mobile network traffic using neural networks based on conditional probability modeling between adjacent data windows. Firstly, we develop a pre-processing method to aggregate the raw traffic log data and sample the aggregated time series to adjacent data windows, as training samples. Secondly, we use neural networks to parameterize the conditional probability between adjacent data windows and estimate the probability by training the neural networks with sampled data. The estimated conditional probability is then used to ensemble the prediction. Thirdly, we show theoretically that the prediction based on all historical data is equivalent to the prediction based on just previous data window, given the estimation of conditional probability between adjacent data windows. We also analyze computation complexity and show that seasonality will reduce the computational complexity. In the experiment, we compare the prediction performance among the models with different seasonality, sample size and number of hidden layers, and show that the proposed schemes achieve better prediction accuracy than state-of-the-art.

In communication networks, the existence of fractional factors can be characterized as the feasibility of data transmission. When some nonadjacent nodes with each other are damaged and a special channel is assigned, the possibility of data transmission in a communication network is equivalent to the existence of fractional ID-factor-critical covered graph. As a consequence, the existence of fractional ID- [ a , b ] -factor-critical covered graph plays a key role in studying data transmissions of communication networks. Neighborhood and minimum degree of a graph or a network are often used to measure the vulnerability and robustness of a graph or a network, which are two important parameters considered in network design. In this article, we mainly investigate the relationship between neighborhood of independent set, minimum degree and the fractional ID- [ a , b ] -factor-critical covered graph, and acquire a neighborhood of independent set and minimum degree condition for a graph being fractional ID- [ a , b ] -factor-critical covered, which is a generalization of the previous results.

In liner shipping, containers are generally transshipped in major hub ports in each region, between larger inter-region vessels and smaller feeder vessels. In this paper, we study the problem of designing the feeder vessel network, transporting containers between a regional hub and the surrounding feeder ports. The problem, as modelled, has many similarities with the split delivery vehicle routing problem, but with additional characteristics such as simultaneous pickups and deliveries and weekly departures. The problem also includes fleet sizing with a heterogeneous fleet and allows demand rejection at a penalty cost. We present a branch-and-price framework to solve the problem, where the subproblem is solved by enumerating vessel routes and subsequently assigning commodities by solving a min-cost flow problem for each commodity. The algorithm is tested on instances with up to 12 ports, which are all solved to optimality. Since the problem is similar to the liner shipping network design problem but without transshipments, we further study selected instances from the LINER-LIB instance suite. The Baltic instance is solved to proven optimality and we find the best known solution to the West Africa instance, significantly improving on what can be found in the literature.

We present a spectral approach to design approximation algorithms for network design problems. We observe that the underlying mathematical questions are the spectral rounding problems, which were studied in spectral sparsification and in discrepancy theory. We extend these results to incorporate additional non-negative linear constraints, and show that they can be used to significantly extend the scope of network design problems that can be solved. Our algorithm for spectral rounding is an iterative randomized rounding algorithm based on the regret minimization framework. In some settings, this provides an alternative spectral algorithm to achieve constant factor approximation for the classical survivable network design problem, and partially answers a question of Bansal about survivable network design with concentration property. We also show many other applications of the spectral rounding results, including weighted experimental design and additive spectral sparsification., Comment: Improved bound on one-sided spectral rounding by a randomized swapping algorithm. Added the proof of the deterministic algorithm for additive sparsifers

We explore a novel optimization model to support the planning and operation of an air service network at a package express carrier in China. The model simultaneously considers ground and air transportation, company-owned aircraft capacity and belly capacity purchased on commercial aircraft, multiple service classes for packages, and packages becoming available throughout the day. A column generation based algorithm is devised to solve real-life problems with more than 650,000 timed origin-destination demands (packages), a service network with 150 hubs at airports, and an air cargo fleet of 60 aircraft of varying capacities. An extensive computational study shows the efficacy of the model and algorithm, and provides managerial insight. In particular, we see that considering ground transportation when designing an air service network is critical to improving operational efficiency and company profits as well as to extending service coverage. Furthermore, we find that in an environment with ample and reasonably-priced capacity on commercial aircraft, an air service network design based on single-flight shipments can be more effective than the commonly used star network design with multi-flight shipments.

Demand uncertainties are inevitable in transportation networks. The transit priority network design problem over more than a decade of development has been solved under deterministic conditions. This paper proposes a model to find the optimal transit priority scheme in a multimodal transportation network under uncertain demand. This model is formulated as a risk-based bi-level optimization problem. At the upper-level, a risk measure of expected social cost is minimized subject to a chance constraint on total travel time with a user-specified confidence level and a budget constraint. At the lower-level, a mode choice, a traffic user equilibrium assignment, and a transit assignment are applied. An ant colony algorithm is utilized to solve this complex design problem. Numerical results using a real world middle-size city network empirically demonstrate that the demand uncertainty has a significant impact on the solution and the proposed model is applicable to realistic networks.

Well-designed multi-modal transportation networks are crucial for our connected world. For instance, the excessive construction of railway tracks in China, at speeds up to 350 km/h, makes it necessary to consider the interaction of rail with air transportation for network design. In this study, we propose a model for an air/rail multi-modal, multiple allocation hub location problem with uncertainty on travel demands. Our model is unique in that it integrates features from the existing literature on multi-modal hub location problem (including hub-level capacities, link capacities, direct links, travel cost and time, transit costs and uncertainty), which have not been considered simultaneously, given its high computational complexity. We formulate this model with O(n⁴) variables and show that the implementation of a Benders decomposition algorithm is inherently hard, because of the cubic number of variables in the master problem. Furthermore, we derive an iterative network design algorithm and additional improvement strategies: MMHUBBI which resolves a restricted problem by the solver CPLEX and MMHUBBI-DIRECT which re-designs the transportation network by a heuristic. Our evaluation on real-world dataset for Chinese domestic transportation shows that MMHUBBI provides a significant speed-up on all instances, compared to using CPLEX, while obtaining near-optimal solutions. MMHUBBI-DIRECT further reduces the runtime/memory usage but provides solutions with worse quality. We believe that our study contributes towards the design of more realistic multi-modal hub location problems.

Building upon earlier research, we revisit a bilevel formulation of service design and pricing for freight networks, with the aim of investigating its algorithmic aspects. The model adds substantial computational challenges to the existing literature, as it deals with general integer network design variables. An iterative heuristic algorithm is introduced, based on the concepts of inverse optimization and neighbourhood search. The procedure alternates between two versions of restricted formulations of the model while inducing promising changes into the service assignments. The approach has proven a high performance for all of the considered real-world instances. Its efficiency rests on its ability to deliver results within a close proximity to those obtained by the exact solver in terms of quality, yet within a significantly smaller amount of time, and to land feasible solutions for the large-sized instances that could not be previously solved. In line with the sustainable transport goals, a deeper observation of the transport management side highlights the strategy of the algorithm favouring freight consolidation and achieving high load factors.

International audience; The objective of the paper is to study the integration of revenue management considerations into service network design models targeting the tactical planning of intermodal consolidation-based freight transportation carriers. Revenue management strategies and mechanisms are broadly used within passenger transportation. Although identified as a desirable feature for freight transportation, interest growing within the industry, few contributions have addressed the topic. Moreover, almost none of those target the challenging issue of the interactions between the planning of the carrier’s services and operations, on the one hand, and the revenue-management strategy it could implement, on the other hand. We propose a new scheduled service network design model with resource and revenue management model, which selects the services and schedule to be repeatedly operated over the next season, allocates and routes the main resources supporting the selected services, and routes the demand flows between their respective origins and destinations. The objective of the model is the maximization of the expected net revenue of the carrier when several customer categories, with specific service requirements, as well as several tariff and operation classes are considered. Our interest goes beyond the modeling challenges raised by the problem setting, to exploring the impacts of this new approach on the decision types and on the structure of the service network solutions obtained. The results of extensive experiments, in terms of demand distribution, network topology, fare class and quality-of-service, provide a proof of concept of the proposed modeling framework and its capability for insightful analyses. Experimentation was conducted using an off-the-shelf software to solve the corresponding mixed-integer linear programming formulation for realistically dimensioned barge intermodal transportation instances.

Most real-world networks are time-varying, and many are subject to the stochastic functioning of their nodes and edges. Examples can be seen in the human brain undergoing an epileptic seizure, spontaneous infection and recovery in epidemics, and intermittent functioning of devices in the Internet of Things. Moreover, such networks are becoming increasingly large due to rapid technological advances. However, little has been done to study time-varying, large-scale, stochastic networks (SNs) from a reliability engineering perspective. Toward this goal, this article develops a fault-tolerance model for a type of time-varying network in which nodes (and/or edges) stochastically switch between active and inactive states. It considers fault tolerance from a global connectivity point of view, which has applications in many natural and engineered networks. Specifically, this article presents a Markov chain framework that models the dynamic behavior of nodes and allows for the computation of quantitative measures, including availability and time-to-failure metrics. To accommodate large-scale networks and emphasize global connectivity, this framework utilizes percolation theory, which has recently been of interest in the reliability engineering discipline, to characterize network failure. This article makes several contributions: it proposes a Markov chain framework for computing fault-tolerance metrics that is tractable for large-scale networks, it shows the existence of a phase transition in network availability of a time-varying SN, and it accounts for finite-size effects of percolation in the fault-tolerance model. The proposed methodology is applied to Erdos-Renyi random graphs and a real, large-scale power grid. Experimental results provide insights into network design, maintenance, and failure prevention of time-varying SNs.

Exclusive bus lanes (XBLs) have been widely discussed and implemented worldwide in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence eliminate the impacts of slowly moving buses on the car traffic. Compared to XBLs, the application of IBLs has not been broadly adopted in the real world so far except very few cases, because managing an urban IBL segment or area typically involves extra traffic signals and message signs and the switch between its regular and protected statuses could potentially cause unexpected traffic chaos and safety issues. The recent emergence of connected and automated vehicle (CAV) technologies, however, favors relieving these concerns and bring the dawn to a future application of this theoretically sound traffic control strategy. This paper is dedicated to evaluating the network performance of XBLs and IBLs and optimizing the networkwide configurations of these two strategies for future urban traffic networks where CAVs prevail. For this purpose, we formulate a system-optimal dynamic traffic assignment (SO-DTA) problem with car-exclusive lane segments, on the basis of a cell transmission model for separate car and bus traffic (CTM-SCB). By limiting its applicable context to morning commute networks and emergency evacuation networks, this model is set with only one real or virtual destination, which greatly eases its modeling and solution complexity. On the basis of the single-destination SO-DTA model, an XBL-based network design problem and an IBL-based network design problem are then both formulated into mixed integer programming models and solved by a discrete optimization solver, where the former problem statically configures bus lanes while the later one allows a dynamic allocation of bus lanes. Insightful findings obtained by applying the models and solver to a synthetic corridor network and a real commute network illustrate the great promise of these two lane-based strategies in mixed car and bus traffic management.

In typical operations research courses, optimization problems, such as transportation and assignment problems, are frequently discussed and taught as stand-alone problems. An integrated approach may prove to be necessary in order to enable students to have a holistic understanding of a complex problem (e.g., a project). In this paper, a global supply network design problem is presented where the case company can source from multiple “suppliers” using multiple modes of transport (including the use of containers with different capacities), allowing lateral supply between warehouses, etc. As more factors are considered, the problem becomes much more complex than any isolated problem in a typical course. The case was tested in an undergraduate course in Australia, and students found this case challenging but at the same time rewarding once solved.

In the periods following sales on online shopping carnivals, demand for express transportation is expected to precipitously surge within a short time, potentially causing express system overload and delivery delays. In this study, we address a service network design problem of a hub-and-spoke structured express transportation network during periods of surging demands. We consider a new strategy of upgrading existing spokes into temporary hubs to expand the network capacity with low costs. We propose a two-stage bi-objective robust model based on a time-space network to integrate the spoke-upgrading selection, service-scheduling, and vehicle-routing for designing an express service network that is both efficient and economic. To solve the model, we develop an enhanced column-and-constraint generation algorithm. Finally, a case study of a real express logistics company in China is demonstrated to validate the effectiveness of the proposed strategy and to offer decision support in real problems.

We consider the following general network design problem. The input is an asymmetric metric (V, c), root [Formula: see text], monotone submodular function [Formula: see text], and budget B. The goal is to find an r-rooted arborescence T of cost at most B that maximizes f(T). Our main result is a simple quasi-polynomial time [Formula: see text]-approximation algorithm for this problem, in which [Formula: see text] is the number of vertices in an optimal solution. As a consequence, we obtain an [Formula: see text]-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved [Formula: see text]-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio but improves significantly on the running time. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first nontrivial approximation ratio. Under certain complexity assumptions, our approximation ratios are the best possible (up to constant factors).

Previous video salient object detection (VSOD) approaches have mainly focused on the perspective of network design for achieving performance improvements. However, with the recent slowdown in the development of deep learning techniques, it might become increasingly difficult to anticipate another breakthrough solely via complex networks. Therefore, this paper proposes a universal learning scheme to obtain a further 3% performance improvement for all state-of-the-art (SOTA) VSOD models. The major highlight of our method is that we propose the ‘motion quality’, a new concept for mining video frames from the ‘buffered’ testing video stream for constructing a fine-tuning set. By using our approach, all frames in this set can all well-detect their salient object by the ‘target SOTA model’ — the one we want to improve. Thus, the VSOD results of the mined set, which were previously derived by the target SOTA model, can be directly applied as pseudolearning objectives to fine-tune a completely new spatial model that has been pretrained on the widely used DAVIS-TR set. Since some spatial scenes in the buffered testing video stream are shown, the fine-tuned spatial model can perform very well for the remaining unseen testing frames, outperforming the target SOTA model significantly. Although offline model fine tuning requires additional time costs, the performance gain can still benefit scenarios without speed requirements. Moreover, its semisupervised methodology might have considerable potential to inspire the VSOD community in the future.

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

The architecture of AND/OR fuzzy neural networks exhibits outstanding learning abilities and significant interpretation capabilities. However, AND/OR networks suffer from structure-related problems namely low efficiency and slow convergence of learning due to several reasons such as high dimensionality and gradient-based learning algorithms which lead to a visible computing overhead. In this paper, we present a two-phase fuzzy logic-oriented network design that is composed of AND/OR neurons. This design takes advantages of Randomized Neural Network (RNN) to achieve higher convergence while exhibiting good nonlinear approximation capabilities. A gradient–based learning algorithm is implemented in the second phase of the design to further reduce values of performance index. The quality of the proposed design and resulting architecture is quantified through the use of numeric data along with fuzzy sets (information granules). Experimental results meet the research’s objectives and the proposed design methodology opens up new future directions for proceeding with more improvements.

It is shown that compensation network and coil design collectively affect wireless power transfer efficiency. Based on this fact, a novel design approach of wireless power transfer (WPT) systems is proposed. Unlike majority of existing designs which aim at enhancing power transfer efficiency (PTE) at one rated operating point, the proposed design enhances the overall system PTE. This feature is essential for variable output loads such as batteries, where their internal impedance varies during charging period. To achieve this goal, improving time-weighted average efficiency of WPT system is set as the design criterion. Experimental studies of a 1.2 kW WPT system supplying constant resistive loads and battery loads verify that the proposed design enhances PTE both at rated output load and under wide output load variations.

This chapter builds on Chapters 2 and 4 and describes more advanced concepts, representations, procedures and graphical diagrams related to the analysis and design of heat recovery systems for both grassroots and retrofit situations. The grassroots case is extended to add more industrial realism, and to bring investment costs into the discussion. Key elements of the grassroots component are Multiple Utilities, Heat Transfer Area considerations, additional design tools beyond the Pinch Design Method, forbidden matches and an introduction to the use of optimisation for heat exchanger network design. Key elements of the retrofit part are procedures for Targeting, Design and Optimisation, where in all these stages focus is on maximum utilisation of existing equipment, making the task very different from the grassroots case. The Network Pinch concept will also be described, which as the name indicates is a feature of the Heat Exchanger Network (HEN), while the Heat Recovery Pinch is a feature of the stream data.

This paper proposes a method which generates a set of weights to synthesize sum or difference pattern with precisely controlled sidelobe level (SLL), null, and dynamic range ratio (DRR) for arbitrary arrays. Our pattern synthesis approach reduces mutual coupling between the neighboring elements and complexity of the feeding network design. However, the formulated optimization problem is nonconvex due to the nonconvex objective function and fractional DRR constraint. To tackle it, we firstly introduce two sets of auxiliary variables: one for DRR constraint and the other for sidelobe and null constraints. By doing so, we then decompose the original optimization problem into three sets of subproblems characterized by the auxiliary variables and weight variables. To facilitate the subproblem with weight variables reaching its optimum values, we derive an appropriate range of step size. Finally, we iteratively solve these subproblems to obtain the solution to the original problem. Extensive experiments employing non-equispaced linear and rectangular arrays, concentric ring array, and cylinder array, are implemented to demonstrate that the developed approach can accurately control SLLs, null and DRR for arbitrary arrays.

The pricing and reverse logistics network design problem in remanufacturing has attracted considerable attention in recent years due to increasingly serious environmental problems. In this study, we consider a pricing and reverse logistics network design problem with price-dependent return quality uncertainty. To handle the high uncertainty in return quality, we propose a distributionally robust risk-averse model to safeguard the profits of investors in extreme situations. We propose a Benders decomposition approach to solve the proposed model. It is enhanced through valid inequalities, local branching, in-out variant methods and scenario-based aggregated cuts. Computational experiments demonstrate that the distributionally robust model can effectively hedge against high uncertainty and that the enhanced Benders decomposition methods significantly outperform their classical counterparts and the off-the-shelf solver Gurobi. Lastly, managerial insights are explored, and future research directions are outlined.

Blockchain based cryptocurrencies have received great attention but long confirmation times of transactions hinder its adoption for micro-payments. Payment network is one of the solutions proposed to address the scalability issue. It transforms the problem into a routing problem by leveraging Hash Time Locked Contracts (HTLC). Payment networks possess unique characteristics to be considered when routing protocol is designed. Channels must stay balanced to have a sustainable network. In this paper, we present a payment network design that aims to keep the channels balanced by using a common weight policy across the network. We additionally utilize multi-point connection for unbalanced payment scenarios. The results show that we can keep the channels in the network more equally balanced compared to minimal fee approach and multiple connections from customers to stores increases the success ratio.

The echelon utilization of retired batteries has attracted more attention as the market share of electric vehicles (EVs) increases steadily year by year. The current battery recycling methods cannot utilize the remaining value of these retired batteries effectively. Studies have shown that the Second-Life Batteries (SLBs) have a much lower cost compared to fresh batteries but retain a certain capacity simultaneously, which means they have a high secondary utilization value. Therefore, this paper proposes a novel multi-stage system planning model including Battery Energy Storage System (BESS). BESS designed in this paper involves not only the use of fresh batteries but also the echelon utilization of SLBs and considers the multiple lifespan cycles based on its State of Health (SOH). Moreover, the proposed planning strategy integrates the electricity and transportation networks in the first stage and evaluates the flexibility of the planning system by comparing the adaptation costs of each system in the second stage. The proposed planning model is verified on the IEEE 33-bus electricity system and the 20-node transportation system. According to the simulation results, the differences in system cost and system operation situations caused by fresh batteries and SLBs are analyzed, verifying the effectiveness and flexibility of echelon utilization of SLBs.

With the increasing demand for rail transit, wireless communication technologies are playing a growing significant role in train control systems, which enables the railway systems to provide a higher capacity and more efficient services. However, due to the nature of radio frequency propagation, the quality of the train-to-ground wireless connections is highly dependent on a well-planned deployment of the wayside access points. To improve both the accuracy and the efficiency in railway network planning, in this paper, a deep learning technology is exploited to model the wireless propagation, which was very difficult to deterministically predict at a fast speed in our previous research due to the high computation demanding. In this proposed wireless propagation model, Kalman filter is utilized to update the neural network parameters online, which makes this model can meet the variation of the environment. The numeric evaluation result shows that the deep neural network based wireless channel model can precisely predict the outage probability with a very low computational cost.

In telecommunications, network service accessibility as a requirement is closely related to equitably serving the population residing at locations that can most appropriately be described as remote. Remote connectivity, however, would have benefited from a more inclusive consideration in the existing generations of mobile communications. To remedy this, sustainability and its social impact are being positioned as key drivers of sixth generation's (6G) research and standardization activities. In particular, there has been a conscious attempt to understand the demands of remote wireless connectivity, which has led to a better understanding of the challenges that lie ahead. In this perspective, this article overviews the key challenges associated with constraints on network design and deployment to be addressed for providing broadband connectivity to rural areas, and proposes novel approaches and solutions for bridging the digital divide in those regions., Comment: This paper has been accepted for publication in IEEE Wireless Communications. 9 pages, 5 figures, 1 table

This study addresses a healthcare facility location/network design problem considering equity and accessibility. Locating the healthcare facilities, planning the capacity of healthcare facilities, and designing the transfer network are the primary goals of the given problem. The presented model aims to minimize system costs, maximize accessibility, and minimize inequality among all demand nodes. A real-world case study is presented to elucidate the performance and applicability of the proposed model. Moreover, a fix-and-optimize (FO) approach is proposed for tackling the problem on a large scale. The obtained results from experiments on several test problems indicate the efficiency of the developed FO.

Internet of thing (IoT) devices are increasingly growing in mobile networks with the ubiquity of various IoT services. They share the same infrastructure with smartphones while having different requirements for communication resources and security defense mechanisms. Distinguishing IoT devices from smartphones has far-reaching implications on effective network design, resource allocation scheme, pricing scheme etc. In this paper, we distinguish between 12,107 IoT devices and 12,693 smartphones in the real world via characterizing their network traffic. The IoT devices fall into five categories, namely locating, monitoring, portable, point of sale (POS), and vehicle. We analyze the device behaviors from network domain, physical domain, and time domain, make comparisons between each kind of IoT devices and smartphones, and design effective features based on the distinguishable network behavior characteristics at packet level, traffic level, and mobility level. Then we train several classifiers based on our feature set to identify different kinds of mobile devices. Specifically, the accuracy of identifying IoT devices from smartphones achieves 95.86%, and the accuracies of distinguishing IoT devices in each category from smartphones are all over 95%. In the trained classifiers, feature importance verifies the discriminability of different network traffic characteristics observed in our multi-domain measurement. Our study reveals the network traffic behavior characteristics for IoT devices, and successfully distinguishes them from smartphones, which paves the way for better network design, resource allocation, pricing scheme, and security defense mechanisms.

In electric drives, the lifetime of power electronics stage is highly worsened using command signals from the speed controllers. The degradation of the power electronics lifetime poses earlier faults which results in the good operation of the electric drives which significantly influence the industrial process. Thus, the power electronic stages of the controllers are generally over designed and thereby reduces the overall performance. In the electric drives, the power electronic components functioning on high switching frequencies in driving high electric power to achieve the expected mechanical references in electric brushless DC (BLDC) motors. In this aspect, this study devises an efficient metaheuristic optimization based fuzzy controller for BLDC lifetime expansion, called MOFC-BLDC technique. The MOFC-BLDC technique's objective is to optimize the network design of the fuzzy logic system. Furthermore, the MOFC-BLDC method has developed an objective function based on speed and reference temperature. Moreover, the inclusion of speed response of the motor and the temperature in the objective function helps in adjusting the fuzzy logic (FL) controller to maximize the lifetime of the power electronic stages. In order to optimally choose the membership functions of the FLC, the Cross Entropy based Butterfly Optimization Algorithm (CEBOA) is used. A wide range of simulations was carried out to highlight the improved outcomes of the proposed MOFC-BLDC technique. The experimental results pointed out the supremacy of the MOFC-BLDC technique over the recent state of art techniques.

In this letter, we investigate the network planning of the heterogeneous aerial networks to maximize the network coverage. Specifically, a joint high-altitude aerial base station (HABS) trajectory and low-altitude aerial base station (LABS) position optimization problem is formulated with the objective to maximize the number of covered users. Based on the feature of the air-to-ground channel, we first obtain the optimal altitude of the LABSs through the bisection method. Then, we propose a joint particle-and-fish swarm algorithm to optimize the horizontal position of the LABS. For the uncovered users, we model the user coverage probability in the HABS and devise a random interval contraction based algorithm to optimize the flying radius of the HABS. Simulation results indicate that our algorithms can fully exploit the mobility of the heterogeneous aerial networks to cover more users.

In many cases, some programs often have some milestone activities that decision makers need to pay attention to. In this case, managers must efficiently describe these different decision-making spaces and use this as a basis to optimize possible solutions. Choosing a suitable decision-making space has always been a major factor in decision-making management, and it is also one of the key factors that managers must determine when making plans. Multi-objective optimization is to optimize multiple objectives at the same time. Multi-objective optimization algorithms have been widely used in many technology industries. This article researched from two parts: model display and optimization algorithm. The first is to deepen the basic characteristics of project planning and decision-making when there are more decision-making spaces. Based on the existing decision-making network planning methods, combined with the situation analysis method, a decision-making network planning model that can show more decision-making space is established. Secondly, based on the actual improvement requirements, the multi-objective optimization method in the multi-decision network design model and the simulation model of activity exercise data are established. The purpose is to create a relatively complete project planning decision model and optimization algorithm that can select multiple decision spaces at the same time through the above experimental research.

Online and omnichannel retailers are proposing increasingly tight delivery deadlines, moving toward instant on-demand delivery. To operate last-mile distribution systems with such tight delivery deadlines efficiently, defining the right strategic distribution network design is of paramount importance. However, this problem exceeds the complexity of the strategic design of traditional last-mile distribution networks for two main reasons: (1) the reduced time available for order handling and delivery and (2) the absence of a delivery cut-off time that clearly separates order collection and delivery periods. This renders state-of-the-art last-mile distribution network design models inappropriate, as they assume periodic order fulfillment based on a delivery cutoff. In this study, we propose a metamodel simulation-based optimization (SO) approach to strategically design last-mile distribution networks with tight delivery deadlines. Our methodology integrates an in-depth simulator with traditional optimization techniques by extending a traditional black-box SO algorithm with an analytical model that captures the underlying structure of the decision problem. Based on a numerical study inspired by the efforts of a global fashion company to introduce on-demand distribution with tight delivery deadlines in Manhattan, we show that our approach outperforms contemporary SO approaches as well as deterministic and stochastic programming methods. In particular, our method systematically yields network designs with superior expected cost performance. Furthermore, it converges to good solutions with a lower computational budget and is more consistent in finding high-quality solutions. We show how congestion effects in the processing of orders at facilities negatively impact the network performance through late delivery of orders and reduced potential for consolidation. In addition, we show that the sensitivity of the optimal network design to congestion effects in order processing at the facilities increases as delivery deadlines become increasingly tight.

With the increasing global communication demands and the development of Internet of Things (IoT), extending the connectivity to rural and remote areas has become imperative for future networks. The sixth generation (6G) network is expected to provide heterogeneous services and seamless network coverage for everyone and everything. Combining the advantages of both satellite and terrestrial networks, the integrated satellite-terrestrial network architecture is promising to provide global broadband access for all types of users, which has drawn much attention from both the academia and industry. In this paper, we present a comprehensive survey of the state-of-the-art of integrated satellite-terrestrial networks towards 6G. First, an executive classification and summary of the integration architecture is presented from network design to performance optimization. Then, typical applications of the integrated satellite-terrestrial network are discussed based on the architecture. By considering the unique characteristics of the two networks, main challenges are pointed out when performing integration, such as the long propagation delay, complex link conditions and high dynamics of the network topology. Finally, some promising future techniques are explored from the perspective of the integrated architecture. A detailed survey of the potential integration architectures is of great importance to enable more flexible network design and construction in future 6G networks. This paper will provide valuable guideline on future research and development of integrated satellite-terrestrial networks.

Neural-network Processing Units (NPU), which specializes in the acceleration of deep neural networks (DNN) inference, is of great significance to latency-sensitive areas like robotics or edge service. However, there are few works focusing on the network design for NPU in recent studies. Most of the popular lightweight structures (e.g. MobileNet) are designed with depthwise convolution, which has less computation in theory but is not friendly to existing hardware, and the speed tested on NPU is not always satisfactory. Even under similar FLOPs (the number of multiply-accumulates), vanilla convolution operation is always faster than depthwise one. In this paper, we will propose a novel architecture named Serial and Parallel Group Network (SPGNet), which can capture discriminative multi-scale information and at the same time keep the structure compact. Extensive evaluations have been conducted on different computer vision tasks, e.g. image classification (CIFAR and ImageNet), object detection (PASCAL VOC and MS COCO) and person re-identification (Market-1501 and DukeMTMC-ReID). The experimental results show that our proposed SPGNet can achieve comparable performance with the state-of-the-art networks while the speed is 120% faster than MobileNetV2 under similar FLOPS and over 300% faster than GhostNet with similar accuracy on NPU.

Robust channel models for indoor areas are a crucial part of network planning and are immensely valuable for the small cell and indoor 5G network evolution. As the main input for many resource allocation and network planning problems, the accuracy of the path loss model can improve the overall accuracy of these techniques. Previous measurement campaigns exist for outdoor areas and higher frequencies, however extensive indoor measurements at these frequencies is missing from the literature. Both WLAN and LTE networks use 2.4 GHz and 5 GHz bands. For this work, indoor measurements were carried out in two distinct indoor environments, at two frequencies, and various models were compared. The measurements were made at the Deutsches Museum Bonn and the ICT cubes, an office space at RWTH Aachen University. Both empirical and deterministic models are tested on the data, the free space path loss model, the single and dual slope models with line-of-sight and nonline-of-sight, ray tracing models, and artificial neural network models were all tested and evaluated. Overall, the artificial neural network combined with the free space path loss model proved to be the most robust model which accurately predicted the propagation in the indoor environments, at both frequencies.

Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this paper, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and energy harvesting. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the CPU frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of user mobility, the EH model, and the channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Optical Wireless Communication (OWC) is being explored for application in the next-generation broadcasting networks, where possessing accurately determined user locations becomes increasingly important. Received signal strength (RSS) Visible Light Positioning (VLP)-based localisation systems aim to deliver these centimetre-level location data at a low cost by featuring but a single photodiode (PD). Maximising the VLP accuracy requires optimising the LED transmitter locations, which is missing currently. An evolutionary optimisation algorithm is proposed to determine the optimal LED locations and the associated positioning error values for various configurations. The sensitivity of the planning on the number of VLP-enabled LEDs, the LEDs' characteristics, the room dimensions and the positioning parameters is investigated. Experimental data, i.e., two datasets with 157(2) measurement points each, serve to validate the simulations.

Heat exchanger network synthesis is widely used for saving energy in the chemical engineering field. In the design stage, a multiple-level strategy is useful for assisting the optimization to guarantee the global search ability and protect the evolution process of the optimal solution. The authors have introduced a tri-level protection strategy based on the Random walk algorithm with compulsive evolution. However, the potential solutions in the middle level may still suffer from the operation of accepting the imperfect solution. The main deficiency of the tri-level protection strategy is that solutions are transferred to the elite level only from the protective level, ignoring the solutions with lower cost yield at the basic level. The strategy failed to fulfil the perfect protection and caused low computational efficiency. Therefore, an improved treble-level assisting optimization method is proposed in this paper, where the function of each level is redefined. Based on the advantages of the previous work, the efficiency is improved in the proposed strategy. The quantified data shows that the two cases are 2,782 $/yr and 7,134 $/yr lower than those reported in the literature, which verifies that the proposed strategy strengthens the global and local search abilities.

Objectives. The quality of a radio receiver preselector largely determines its main characteristics, including sensitivity. A preselector usually consists of linear elements: inductors, capacitors, low noise amplifiers, and switches. At high frequencies, the components cannot be considered as ideal ones, since active and reactive parasitic parameters significantly affect the frequency response of the components and, as a consequence, the network. Therefore, simulation of the networks requires more sophisticated component models, which take into account parasitic parameters. However, if refined components models are applied, it is still possible to obtain unsatisfactory results, since interconnections and footprints pads also affect the frequency response. This is true even if short lines with a length of about 5 mm are used at frequencies of about 100 MHz. These features must be taken into account for RF network design. The purpose of the work is to ensure the required characteristics of the preselector in the design process based on computer simulation.Methods. Egor Gurov’s methodology for analog VHF LC-filters was applied to radio receiver preselector design. The methodology contains the methods of discrete optimization, Monte-Carlo method, momentum analysis with Green’s functions. Experimental results were obtained by prototype implementation and measurement with a vector network analyzer. The purpose of the work is to ensure the required preselector characteristics in the design process based on computer simulation.Results. The article presents the preselector design process. The preselector contains two analog switches, an analog band-pass filter, an analog high-pass filter, and a low-noise amplifier. Simulation and experimental results with their comparison are presented in the article.Conclusions. Satisfactory results were obtained. It means that Egor Gurov’s method can be applied for more complex networks design such as radio receiver preselectors.

Bike Sharing Systems (BSS) have gained popularity all over the world. BSS success requires efficient location of stations and the rebalancing of the network, both of which depend on predicted demand and flows between stations. Therefore, predicting BSS demand is essential to optimize logistic operations, as well as network reliability and availability. The present work develops statistical models to predict the hourly origin-destination demand in the Lisbon BSS network. Models predict the number of trips that will occur between an origin station and a destination station (OD pair) at a certain hour, as well as the duration of such trips. This study also examines the effect of several explaining variables, including the weather, intermodality effects, effects of station location close to different types of points of interest and the time/hour effect. These effects were all tested using different statistical models, including the Generalized Linear Models (GLM) and zero-augmented ones: the hurdle model and the zero-inflated model. The hurdle model showed the best fit to the data and several explaining variables exhibited statistical significant effects. A discussion on the potential logistic and policy impacts of such findings is also provided, so that BSS network design and operations can incorporate them within the urban mobility system policy.

In many countries distributed energy resources (DER) (e.g. photovoltaics, batteries, wind turbines, electric vehicles, electric heat pumps, air-conditioning units and smart domestic appliances) are part of the ‘Green Deal’ to deliver a climate neutral society. Policy roadmaps, despite providing a framework and penetration targets for DER, often lack the network planning strategies needed to transition from passive to active distribution networks. Currently, DER's dynamic performance parameters and location identification techniques are not fully standardised. In fact, it can be very ad hoc. Standardised distributed load modelling and non-intrusive load monitoring (NILM) for equipment manufacturers, installers and network operators is critical to low and medium voltage network management in order to facilitate better balancing, flexibility and electricity trading across and within the power system for mass DER deployment. The aim of this paper is to fill this load modelling and NILM knowledge gap for DERto inform the ‘Green Deal’ transition and support standardisation. In the paper, existing load modelling techniques and NILM methodologies are critically examined to inform and guide research activity, equipment development and regulator thinking, as well as network operators. Seven key findings that need urgent attention are identified to support a smooth power system reconfiguration.

Radio frequency identification (RFID) is a widespread technology used in several different industries. One of its common use cases in logistics is the automation of goods receipt. RFID gates are often deployed, to automatically detect tagged items or load carriers during their passage through the goods receipt gate. At present, however, the design of RFID gates is often based on estimations, and their commissioning is mostly approached via trial and error. Even if the RFID network planning problem is known in the literature, existing algorithms cannot be applied to the design of RFID gates due to some limitations. In this paper, we propose a new evolutionary RFID network planning algorithm to design RFID gates optimally. The objective of our algorithm is to minimise the number of antennas and to adjust their mounting heights and angles. The algorithm ensures a tag coverage of at least 99%, prevents reflections on the ground, and can be used in the future as a standard for planning and commissioning RFID-enabled goods receipt gates. To demonstrate the applicability of our algorithm, we deployed it in a case study involving logistics of the automotive sector. The results of the deployment confirm the quality of our approach, as the RFID gate optimised by the algorithm deployed 4 antennas, with a vertical coverage rate of 99.96%, an horizontal coverage rate of 89.66%, and very interesting values of other evaluation functions, namely load balance and overlapping rate.

This paper aims to develop a sustainable closed-loop supply chain network model with a modular single product which is specifically designed for the automotive industry. Drawing upon sustainability criteria, three objective functions are formulated for the closed-loop network design problem including maximizing total profit across the network, minimizing the effects of environmental pollutants and maximizing employments created by the establishment of the required facilities and also maximizing the weighted sum of the minimum distance of facilities from the residential areas. In order to validate the research, a case study of the Iran’s automotive industry is also conducted. In addition, a scenario-based approach which applies the stochastic programming is used to cope with the uncertainty in both the demand and the amount of returned unusable vehicles. The results show that the stochastic programming approach is successful in mitigating the effects of uncertainties. Moreover, augmented $$\varepsilon$$ -constraint methods are applied to deal with the proposed model. The preferred Pareto optimal solution achieves a 55.1% decrease in the environmental objective value, with only 0.2% increase in the economic objective value relative to the corresponding optimal value.

Industrial Internet-of-Things (IIoT) applications require reliable and efficient wireless communication. Assuming dense wireless sensor networks (WSNs) operating in a harsh environment, a concept of a time-division multiple access (TDMA)-based WSN enriched with electronically steerable parasitic array radiator (ESPAR) antennas is proposed and examined in this work. The utilized antenna provides one omnidirectional and 12 directional radiation patterns that can be electronically switched by the sensor node. We introduce a relay discovery algorithm, which selects those sensor nodes with an ESPAR antenna capable to act as relay. The selection of the relay nodes is based on a certain link quality threshold that algorithm uses as input. The outcome is a reduction in the number of layers or hops with a guaranteed Quality of Service (QoS). To emphasize the physical aspect of the wireless propagation, we introduce the measured antenna radiation patterns and consider two different path-loss propagation models representing blockage-free and blockage-prone industrial environments. A number of network simulations were performed and signal-to-noise ratio (SNR) as a link quality measure was examined with respect to the network density and different measured radiation pattern settings. The main outcomes show a tradeoff between SNR per link and the percentage of nodes that can serve as relays. As a result, we propose network design guidelines that take under consideration the QoS range with respect to SNR together with an optimal number of antenna radiation patterns that should be selected as a tradeoff between latency, energy consumption, and reliability in a network.