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Automatic landing of aircraft is a challenging guidance and control task that requires multiple feedback loops and a precise sequence of actions. Increasing operational flexibility and reducing the...

The optimal dispatch of electric vehicles (EVs) aims to minimize the system operation cost while satisfying the requirements for peak shaving, congestion management and voltage regulation. However, the stochastic mobility of EVs makes dispatch difficult and requires modeling the spatial and temporal distribution of EV availability. In this paper, a trip-chain-based EV resource aggregation model considering EV flexibility similarity is developed. Then, a bilevel optimization model is formulated to enable participation of the EV aggregators (EAGGs) in the day-ahead dispatch while ensuring various system operation constraints. Finally, the proposed bilevel model is transformed into a single-level convex optimization problem that can be conveniently solved by off-the-shelf software. Simulation tests substantiate that the proposed approach is superior to the existing method in terms of higher computational efficiency and lower system operation cost.

It is not possible to establish the absence of a population with certainty using imperfect zero-sighting records, but absence can be inferred. In this paper we use Bayesian methods to formulate analytical inferred distributions and statistics. When such formulations are available, they offer a highly efficient and powerful means of analysis. Our purpose is to provide accessible and versatile formulations to support an assessment of population absence for management decisions, using data from a series of regular and targeted surveys with zero-sightings. The stochastic processes considered here are prior population size, growth and imperfect detection, which are combined into a single distribution with sufficient flexibility to accommodate alternative distributions for each of the driving processes. Analytical solutions formulated include the inferred mean and variance for population size or number of infested survey-units, the probability of absence, the probability of a series of negative surveys conditional on presence, and the probability a population is first detected in a given survey, although we also formulate other statistics and provide explicit thresholds designed to support management decisions. Our formulation and results are straightforward to apply and provide insight into the nonlinear interactions and general characteristics of such systems. Although motivated by an assessment of population absence following a pest eradication program, results are also relevant to the status of threatened species, to ‘proof-of-freedom’ requirements for trade, and for inferring population size when a population is first detected.

Sketches, as a kind of probabilistic data structures, have been considered as the most promising solution for network measurement in recent years. Most sketches do not work well for skewed network traffic. To address this problem, we propose a family of sketch frameworks, namely the Pyramid family. The first member of our Pyramid family is the S-Pyramid framework, which includes two techniques: counter-pair sharing for high accuracy, and word acceleration for fast speed. The second member of our Pyramid family is the Mini-Pyramid framework, which projects the S-Pyramid framework into one counter, bringing more flexibility in application while keeping the accuracy. To demonstrate the generality of our Pyramid family, we apply both frameworks to sketches of CM, CU, Count, and Augmented. To demonstrate the flexibility of the Mini-Pyramid framework, we further apply Mini-Pyramid to SBF and the On-Off sketch. The experimental results show that, the S-Pyramid framework can reduce the ARE by up to 7.12 times compared with the original sketches, while improving the throughput by up to 2.37 times; the Mini-Pyramid framework can reduce the ARE by up to 29.2 times, at the cost of 21.3% lower throughput on average.

Speed estimation schemes based on the closed-loop synchronization (CLS) methods for speed-sensorless control of motor drives attract much popularity due to several advantages, e.g., easy implementation, high flexibility, and acceptable performance. However, most of the existing CLS-based estimation schemes may suffer from performance degradation during frequency ramps. Considering this, an attempt of the type-3 phase-locked loop (PLL)-based scheme is made. This solution, however, may adversely affect the system dynamics and stability margin. To address these issues, an open-loop synchronization (OLS) method is proposed for speed-sensorless control of induction motor drives in this paper. In the proposed scheme, the estimated speed is obtained according to the sinusoidal signals and their time-delay signals, rather than increasing the system order. With this, system dynamics and stability margin are maintained. In practice, the disturbance of DC offsets is of concern in induction motor drives. Thus, a closed-loop flux observer is adopted to guarantee the estimation performance under DC offsets. The performance of the proposed OLS scheme is investigated and compared with that of the CLS schemes and the type-3 PLL scheme through experimental tests.

High penetration of renewable energy such as wind power and photovoltaic (PV) requires large amounts of flexibility to balance their inherent variability. Making an accurate prediction of the future power system imbalance is an efficient approach to reduce these balancing costs. However, the imbalance is affected not only by renewables but also by complex market dynamics and technology constraints, for which the dependence structure is unknown. Therefore, this paper introduces a new architecture of sequence-to-sequence recurrent neural networks to efficiently process time-based information in an interpretable fashion. To that end, the selection of relevant variables is internalized into the model, which provides insights on the relative importance of individual inputs, while bypassing the cumbersome need for data-preprocessing. Then, the model is further enriched with an attention mechanism that is tailored to focus on the relevant contextual information, which is useful to better understand the underlying dynamics such as seasonal patterns. Outcomes show that adding modules to generate explainable forecasts makes the model more efficient and robust, thus leading to enhanced performance.

Agile quadrotor flight in challenging environments has the potential to revolutionize shipping, transportation, and search and rescue applications. Nonlinear model predictive control (NMPC) has recently shown promising results for agile quadrotor control, but relies on highly accurate models for maximum performance. Hence, model uncertainties in the form of unmodeled complex aerodynamic effects, varying payloads and parameter mismatch will degrade overall system performance. In this paper, we propose L1-NMPC, a novel hybrid adaptive NMPC to learn model uncertainties online and immediately compensate for them, drastically improving performance over the non-adaptive baseline with minimal computational overhead. Our proposed architecture generalizes to many different environments from which we evaluate wind, unknown payloads, and highly agile flight conditions. The proposed method demonstrates immense flexibility and robustness, with more than 90% tracking error reduction over non-adaptive NMPC under large unknown disturbances and without any gain tuning. In addition, the same controller with identical gains can accurately fly highly agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking performance improvements of around 50% relative to the non-adaptive NMPC baseline., 8 Pages, 6 figures, Accepted RAL 2021

SUMMARYWhen faced with predatorial threats, escape towards shelter is an adaptive action that offers long-term protection against the attacker. From crustaceans to mammals, animals rely on knowledge of safe locations in the environment to instinctively execute rapid shelter-directed escape actions 1,2. While previous work has identified neural mechanisms of escape initiation3–5, it is not known how the escape circuit incorporates spatial information to execute rapid flights along the most efficient route to shelter. Here we show that mouse retrosplenial cortex (RSP) and superior colliculus (SC) form a circuit that encodes shelter direction vector and is specifically required for accurately orienting to shelter during escape. Shelter direction is encoded in RSP and SC neurons in egocentric coordinates and SC shelter-direction tuning depends on RSP activity. Inactivation of the RSP-SC pathway disrupts orientation to shelter and causes escapes away from the optimal shelter-directed route, but does not lead to generic deficits in orientation or spatial navigation. We find that the RSP and SC are monosynaptically connected and form a feedforward lateral inhibition microcircuit that strongly drives the inhibitory collicular network due to higher RSP input convergence and synaptic integration efficiency in inhibitory SC neurons. This results in broad shelter direction tuning in inhibitory SC neurons and sharply tuned excitatory SC neurons. These findings are recapitulated by a biologically-constrained spiking network model where RSP input to the local SC recurrent ring architecture generates a circular shelter-direction map. We propose that this RSP-SC circuit might be specialized for generating collicular representations of memorized spatial goals that are readily accessible to the motor system during escape, or more broadly, during navigation when the goal must be reached as fast as possible.

Logistics capability is an essential capacity for transport and logistics firm to convey the value and services to the client. However, supply chain uncertainty and risk establish the obstacle while achieving operational excellence. The main objective of this research is to examine the impact of logistic capabilities on mitigating supply chain uncertainty and risk in courier firms in Pakistan. Research identified that operational flexibility, innovation and process optimization has a significant positive impact on company side risk and uncertainties whereas process optimization also positively impacted on customer’s side risk and uncertainties. An online survey was conducted and data were obtained from sample size of 150 employees from Pakistani logistics companies. We performed multiple regression analysis to indicate the impact of logistics capability in mitigation of supply chain uncertainty and risk.

Research and development (R&D) project selection is essential for many organizations ; however, it is a complex decision since it is affected by many factors. These factors vary among organizations because of their different objectives and conditions and limited budgets for the investments. Therefore, the main motivation of this article is to create a method to help improve the ex-ante selection of R&D projects in regulated organizations. More importantly, the case application is the electric transmission utilities sector, which plays one of the most critical roles in the entire electric power system. The main objective of this article is to develop a model to select R&D projects based on a holistic approach aligned to strategies, utility objectives, and market conditions in the electric transmission sector. At the same time, it identifies, categorizes, and quantifies the factors associated with R&D projects in the power sector. The analysis is framed into a multicriteria model, hierarchical decision model—HDM proposed by (Kocaoglu, 1983), which considers all the aspects associated with R&D projects. The model and the application are potentially applicable to regulated organizations around the world. Moreover, the flexibility of the model allows it to be adopted by electric transmission utilities with similar characteristics to utilities in the United States. A complete analysis of criteria and subcriteria has been done. There are gaps in the literature that have been identified and that support the idea of using a multicriteria analysis to evaluate R&D projects. The methodology is described, and the application of the model is provided.

Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce its economic risks in the market. This paper develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP profit in the electricity market. Heterogeneity in DR potential across individual customers are considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices and DR resources. Case studies under two market imbalance settlement schemes demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP expected profit under both market settings

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response. We present explicit, integral-equation expressions for the first-passage time distributions of the urgency-gating and collapsing-bounds models and use them to identify conditions under which the models are equivalent. We combine these expressions with a dynamic model of stimulus encoding that allows the effects of perceptual and decisional integration to be distinguished. We compare the resulting models to the standard diffusion model with variability in drift rates on data from three experimental paradigms in which stimulus information was either constant or changed over time. The standard diffusion model was the best model for tasks with constant stimulus information; the models with time-varying urgency or decision bounds performed similarly to the standard diffusion model on tasks with changing stimulus information. We found little support for the claim that evidence does not accumulate and attribute the good performance of the time-varying models on changing-stimulus tasks to their increased flexibility and not to their ability to account for systematic experimental effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Due to the huge gap between the high dynamic range of natural scenes and the limited (low) range of consumer-grade cameras, a single-shot image can hardly record all the information of a scene. Multi-exposure image fusion (MEF) has been an effective way to solve this problem by integrating multiple shots with different exposures, which is in nature an enhancement problem. During fusion, two perceptual factors including the informativeness and the visual realism should be concerned simultaneously. To achieve the goal, this paper presents a deep perceptual enhancement network for MEF, termed as DPE-MEF. Specifically, the proposed DPE-MEF contains two modules, one of which responds to gather content details from inputs while the other takes care of color mapping/correction for final results. Both extensive experimental results and ablation studies are conducted to show the efficacy of our design, and demonstrate its superiority over other state-of-the-art alternatives both quantitatively and qualitatively. We also verify the flexibility of the proposed strategy on improving the exposure quality of single images. Moreover, our DPE-MEF can fuse 720p images in more than 60 pairs per second on an Nvidia 2080Ti GPU, making it attractive for practical use. Our code is available at https://github.com/dongdong4fei/DPE-MEF .

The emergence of CubeSats as a viable means for realizing satellite networks at low costs has given rise to ubiquitous cyber-physical systems spanning air, ground, and space, in what is being recognized as the Internet of Space Things (IoST). IoST is expected to serve a wide variety of applications ranging from monitoring and reconnaissance to in-space backhauling, i.e., the network architecture must serve a plethora of application scenarios with differing service-level agreement (SLA) requirements over the same physical infrastructure in an end-to-end manner. At the same time, since the different use cases might belong to a variety of different stakeholders, IoST must support multi-tenancy and functional isolation of services. Consequently, a network slicing framework is vital to the success of IoST. To this end, an automatic network slicing framework for space-ground integrated networks is presented in this paper. The proposed framework has been designed to address the dual objectives of route computation and resource allocation with minimal SLA violations. Different from the existing state-of-the-art, the framework presented herein is purpose-built for ultra-dense CubeSat networks, and is fully automated. In other words, the framework is purely SLA-based, and does not require prior information concerning the resource requirements associated with a slice. Other key innovations introduced through this framework include a robust SLA model for slice customization, a novel topology construction mechanism, and a unique segment routing-based online admission control solution. Furthermore, the flexibility and efficacy of the proposed framework have been evaluated through a comprehensive use case driven evaluation scenario.

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

Conventional Semi-supervised learning (SSL) approaches are limited by the reliance on assumptions of samples (manifold assumption, cluster assumption and smoothness assumption) and may fail to reach their full potential when it comes to industrial scenarios with complicated data characteristics. In this paper, a semi-supervised process quality evaluation framework is proposed based on conditional variational auto-encoder. Label inference process is utilized to handle unlabeled samples during training stage and to directly predict the label during the evaluation stage. Real NVP (real-valued non-volume preserving) is introduced to optimize the posterior distribution in order to increase flexibility. The proposed framework does not rely on assumptions of data distributions and it learns the hidden distributions from samples. Gear hobbing simulation and experiment investigations are conducted to verify the effectiveness of the proposed framework. The results indicate that the proposed method can achieve remarkable classification performance compared with other state-of-the-art approaches.

The new methodologies to derive discharge limits considering operational flexibility according to international safety standards were developed to help reduce the environmental releases of radioactive effluents from nuclear power plants (NPPs). To overcome the limitations of the two existing methods to set up discharge limits assuming a specific statistical distribution of the effluent discharge, two modified equations were newly proposed to directly derive a particular discharge limits corresponding to the target ‘compliance probability’ based on the actual annual discharge data for a specific NPP and radionuclide groups. By applying these to the actual yearly discharge data of 14 Korean NPPs for 7 radionuclide groups for the past 20 years, the applicability of two new methodologies to actual cases was demonstrated. The ‘characteristic value’ with approximately a 90% compliance probability for each Korean NPP and radionuclide group was proposed based on the results. The new approaches for setting up the discharge limits and the characteristic values developed in this study are expected to be effectively utilized to foster operator's efforts to progressively reduce the environmental releases of radioactive effluents of NPPs relative to the previous discharge data considering operational flexibilities.

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. The objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the KarushKuhnTucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data and defend against cyber-threats. To address these issues, the authors present a distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-aware architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g., throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

Pairing cryptosystems are powerful mathematical tools for the development of cryptographic protocols that provide end-to-end security for applications like Internet-of-Things (IoT), cloud services and cyber-physical systems (CPS). However, these applications require light-weight implementations but still real-time and flexible. The flexibility can come from different choices of underlying algorithms along with suitable parameters. A software implementation offers better flexibility but lacks in timing performance, whereas custom hardware delivers better timing performance but has poor flexibility. Furthermore, the designs over small characteristic curves are now insecure against recent attacks. Existing designs do not address the drawback of less flexibility and massive resource consumption collectively. We present a micro-program controlled hardware design which has the least resource consumption among the comparable existing designs on FPGA. This redundant number arithmetic-based architecture consumes only 2,506 slices on Xilinx Virtex-7 FPGA. It can be migrated to other device families or updated for different algorithms without data-path or control path modification. To enhance the flexibility, we developed a custom assembly-like finite state machine (FSM) description, called Prism, and necessary tool to generate the micro-program states. To illustrate the functionality of Prism, we present designs for Tate and Optimal-Ate pairing with micro-program states generated using this tool.

This paper addresses the cyber resilience issues of multi-vector energy distribution systems (MEDS) caused by false data injection FDI, considering the uncertainty from renewable resources. A novel two-stage distributionally robust optimization (DRO) is proposed to realize the day-ahead and real-time resilience improvement. The first stage determines an initial plan for day-ahead reserve preparation and the second stage makes adjustment and takes resilience-based actions after potential load redistribution (LR) attacks and renewable output deviation. The ambiguity set is based on both the Wasserstein distance and moment information. Compared to robust optimization which considers the worst case, DRO yields less-conservative solutions and thus provides more economic operation schemes. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Case studies are demonstrated on two representative MEDS networked with energy hubs, i.e., a 33-bus-20-node MEDS and a 69-bus-20-node-MEDS, illustrating the effectiveness of the proposed cyber-secured model.

Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system. In this paper, we propose an effective end-to-end framework named Document Enhancement Generative Adversarial Networks (DE-GAN) that uses the conditional GANs (cGANs) to restore severely degraded document images. To the best of our knowledge, this practice has not been studied within the context of generative adversarial deep networks. We demonstrate that, in different tasks (document clean up, binarization, deblurring and watermark removal), DE-GAN can produce an enhanced version of the degraded document with a high quality. In addition, our approach provides consistent improvements compared to state-of-the-art methods over the widely used DIBCO 2013, DIBCO 2017 and H-DIBCO 2018 datasets, proving its ability to restore a degraded document image to its ideal condition. The obtained results on a wide variety of degradation reveal the flexibility of the proposed model to be exploited in other document enhancement problems., Accepted in IEEE TPAMI

In many container terminals, containers are piled vertically and horizontally in the terminal yard, limited mainly by the dimensions of the yard crane. Import and export containers are typically stacked separately. An external truck can access the terminal to pick up an import container only after making an appointment reserving a pickup time. To reduce truck waiting time inside the terminal, container pickup appointments are normally scheduled on a time window basis. However, when a truck arrives at the terminal yard at the appointed time, it is common for the target container not to be at the top of its stack, resulting in unproductive relocations to remove all the containers stacked above the target container and thus increasing the truck's waiting time. To minimize the number of relocations, the Block Relocation Problem (BRP) is usually solved independently, without consideration of appointment scheduling. In this paper, we introduce a new optimization problem—the Block Relocation Problem with Appointment Scheduling (BRPAS)—to jointly address the two issues. To solve the problem, two binary IP models are proposed, and examples from the literature are solved to confirm the performance of the two models. The proposed formulations are further extended to cover several operational aspects related to the flexibility of container pickup operations. Results show that the proposed approach can improve container relocation operations at terminal yards by coordinating with appointment scheduling.

In today’s world, it is important to make sound decisions on generation expansion planning (GEP) for sustainable growth as well as climate risk mitigation. In particular, constraints on carbon emissions reduction have become imperative for various jurisdictions around the world. The challenge is that energy sources have different risk profiles in terms of supply uncertainties, operational (in-)flexibilities, high/low carbon emission rates, rare but extreme accident costs, etc. In this work, we propose a novel model for optimal expansion planning to incorporate this heterogeneity. Trade-offs occur due to different characteristics of energy sources. We study optimal long-term capacity expansion planning while achieving carbon emissions reduction targets. This is accomplished under different risk measures such as mean and value-at-risk. Numerical experiments are conducted based on the data of South Korea, from which we observe notable and combined effects of operational flexibility, emissions reduction, and extremal risks.

The ubiquitous deployment of 5G New Radio (5G NR) stimulates Unmanned Aircraft Systems (UAS) swarm networking to evolve to achieve more imminent progress. The heterogeneous collaboration between UAS swarm enhances the complexity and the efficiency of mission complement that requires robustness, flexibility, and sustainability of throughput in UAS swarm networking. The conventional approaches mainly are based on the hierarchical architectures that are limited to satisfy the challenges of UAS swarm with high dynamics on a large scale. In this paper, we propose an optimal cell wall paradigm to enhance the throughput in heterogeneous UAS swarm networking. With the weight adjustment of each link, we map the optimization into a polyhedron scheduling problem and formula the problem into Max-min Throughput Fair Scheduling (MTFS). Further, we propose a max-min throughput algorithm to optimize the minimum throughput of cell wall paradigm. With the optimal max-min throughput, we optimize the schedule with edge-coloring to achieve global MTFS solving. The normalized MTFS shows our algorithm can achieve over 40% improvement of MTFS globally. In terms of MTFS solving, our algorithms have promising potential to improve the throughput and mitigate the incidents for multiple beams enabling of UAS in cell wall communication. With the throughput enhancement, the advanced aerial mobility of UAS swarm networking can be escalated on a large scale.

The traditional paradigm for pharmaceutical manufacturing is focused primarily upon centralized facilities that enable mass production and distribution. While this system reliably maintains high product quality and reproducibility, its rigidity imposes limitations upon new manufacturing innovations that could improve efficiency and support supply chain resiliency. Agile manufacturing methodologies, which leverage flexibility through portability and decentralization, allow manufacturers to respond to patient needs on demand and present a potential solution to enable timely access to critical medicines. Agile approaches are particularly applicable to the production of small-batch, personalized therapies, which must be customized for each individual patient close to the point-of-care. However, despite significant progress in the advancement of agile-enabling technologies across several different industries, there are substantial global regulatory challenges that encumber the adoption of agile manufacturing techniques in the pharmaceutical industry. This review provides an overview of regulatory barriers as well as emerging opportunities to facilitate the use of agile manufacturing for the production of pharmaceutical products. Future-oriented approaches for incorporating agile methodologies within the global regulatory framework are also proposed. Collaboration between regulators and manufacturers to cohesively navigate the regulatory waters is ultimately needed to best serve patients in the rapidly-changing healthcare environment.

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.

This brief investigates the resilient consensus control for multiagent systems over a time-varying directed random network. We propose a median-based consensus strategy, which is purely distributed and, as opposed to the Weighted-Mean-Subsequence-Reduced approaches in the existing literature, shared estimate regarding the number of malicious agents in the neighborhood of each cooperative agent is not required. This offers more applicability and flexibility as seeking a shared estimate of surrounding threats is often difficult in practice. In addition to malicious agents, random availability of communication edges is accommodated in the random network framework. Sufficient conditions are derived for reaching almost sure consensus by using a martingale convergence theorem. Finally, the theoretical findings are illustrated by numerical simulations.

The growing penetration of hydrogen vehicles and modern energy conversion technologies strengthen the coupling of transportation and energy networks. This paper proposes a hydrogen supply infrastructure planning model for the integration of hydrogen vehicles and renewable energy. To flexibly meet the energy demand of hydrogen vehicles, the proposed model makes investment decisions for various facilities, including hydrogen pipelines, hydrogen refueling stations, power to gas devices, and renewable energy generators. Besides, with the pipeline transportation method applied, the hydrogen network is constructed and coordinated with the electricity and transportation networks. The multi-network synergistic effect is thus fully utilized, and brings higher operational flexibility and investment economy. Furthermore, a two-stage stochastic planning model is provided to accommodate the variability of renewable energy and traffic loads. To reduce the computational complexity of the proposed stochastic planning model, both linearization techniques and Benders decomposition algorithm are applied. Simulation results of the 8-node system and the 24-node system demonstrate the effectiveness of the proposed model and algorithm. Compared to the uncoordinated model, the proposed model saves by 11% of the total cost for the 24-node test system. Also, the computational performance of the Benders decomposition algorithm surpasses that of the basic algorithm by more than 24.6% in the two test systems.

Smart agriculture enables the efficiency and intelligence of production in physical farm management. Though promising, due to the limitation of the existing data collection methods, it still encounters few challenges required to be considered. Mobile crowd sensing (MCS) embeds three beneficial characteristics: 1) cost-effectiveness; 2) scalability; and 3) mobility and robustness. With the Internet of Things becoming a reality, smartphones are widely becoming available even in remote areas. Hence, both the MCS characteristics and the plug-and-play widely available infrastructure provide huge opportunities for MCS-enabled smart agriculture, opening up several new opportunities at the application level. In this article, we extensively evaluate agriculture mobile crowd sensing (AMCS) and provide insights for agricultural data collection schemes. In addition, we offer a comparative study with the existing agriculture data collection solutions and conclude that AMCS has significant benefits in terms of flexibility, collecting implicit data, and low-cost requirements. However, we note that AMCSs may still possess limitations regarding data integrity and quality to be considered a future work. To this end, we perform a detailed analysis of the challenges and opportunities that concerns MCS-enabled agriculture by putting forward seven potential applications of AMCS-enabled agriculture. Finally, we propose general research based on agricultural characteristics and discuss a special case based on the solar insecticidal lamp maintenance problem.

The generation mix of Portugal now contains a significant amount of variable renewable energy sources (RES) and the amount of RES is expected to grow substantially. This has led to concerns being raised regarding the security of the supply of the Portuguese electric system as well as concerns relating to system inertia. Deploying and efficiently using various flexibility options is proposed as a solution to these concerns. Among these flexibility options proposed is the use of battery energy storage systems (BESSs) as well as relaxing system inertia constraints such as the system nonsynchronous penetration (SNSP). This article proposes a stochastic mixed-integer linear programming problem formulation, which examines the effects of deploying BESS in a power system. The model is deployed on a real-world test case and results show that the optimal use of BESS can reduce system costs by as much as 10% relative to a baseline scenario and the costs are reduced further when the SNSP constraint is relaxed. The amount of RES curtailment is also reduced with the increased flexibility of the power system through the use of BESS. Thus, the efficiency of the Portuguese transmission system is greatly increased by the use of flexibility measures, primarily the use of BESS.

The concept of the fifth generation (5G) mobile network system has emerged in recent years as telecommunication operators and service providers look to upgrade their infrastructure and delivery modes to meet the growing demand. Concepts such as softwarization, virtualization, and machine learning will be key components as innovative and flexible enablers of such networks. In particular, paradigms such as software-defined networks, software-defined perimeter, cloud & edge computing, and network function virtualization will play a major role in addressing several 5G networks' challenges, especially in terms of flexibility, programmability, scalability, and security. In this work, the role and potential of these paradigms in the context of V2X communication is discussed. To do so, the paper starts off by providing an overview and background of V2X communications. Then, the paper discusses in more details the various challenges facing V2X communications and some of the previous literature work done to tackle them. Furthermore, the paper describes how softwarization, virtualization, and machine learning can be adapted to tackle the challenges of such networks.

Image cropping aims to improve the composition as well as aesthetic quality of an image by removing extraneous content from it. Most of the existing image cropping databases provide only one or several human-annotated bounding boxes as the groundtruths, which can hardly reflect the non-uniqueness and flexibility of image cropping in practice. The employed evaluation metrics such as intersection-over-union cannot reliably reflect the real performance of a cropping model, either. This work revisits the problem of image cropping, and presents a grid anchor based formulation by considering the special properties and requirements (e.g., local redundancy, content preservation, aspect ratio) of image cropping. Our formulation reduces the searching space of candidate crops from millions to no more than ninety. Consequently, a grid anchor based cropping benchmark is constructed, where all crops of each image are annotated and more reliable evaluation metrics are defined. To meet the practical demands of robust performance and high efficiency, we also design an effective and lightweight cropping model. By simultaneously considering the region of interest and region of discard, and leveraging multi-scale information, our model can robustly output visually pleasing crops for images of different scenes. With less than 2.5M parameters, our model runs at a speed of 200 FPS on one single GTX 1080Ti GPU and 12 FPS on one i7-6800K CPU. The code is available at: \url{https://github.com/HuiZeng/Grid-Anchor-based-Image-Cropping-Pytorch}., Extension of a CVPR 2019 paper. Dataset and PyTorch Code are released. arXiv admin note: substantial text overlap with arXiv:1904.04441

As the penetration of roof-top solar photovoltaics (PV) becomes very large, voltage regulation to the power grid via transactive energy has emerged to achieve system security and flexibility. Several recent studies have extensively studied transac-tive mechanisms that directly control either real or reactive power of the prosumers. However, they fail to fully incentivize prosumers’ participation due to potential power losses (for real-power control) and prosumers’ low regulation capacities (for reactive-power control). This paper addresses this gap by exploiting the capacities of prosumers’ distributed assets through simultaneous control of both real and reactive powers. In particular, a transactive mechanism is proposed for PV prosumers connected to a remote-area distribution feeder to manage their PV inverters for voltage regulation. In this mechanism, customers are given economic benefits for providing voltage regulation services, which are integrated into their operational objectives. The formulated multi-agent structure is modeled as a non-cooperative Nash game, in which the non-differentiable nature of the problem is eliminated by a smooth approximation, and solved with a Nikaido-Isoda function-based descent direction algorithm. The simulation results show that the proposed scheme incurs lower costs for prosumers both individually and collectively, and significantly improves the feeder voltage profile.

Fully decentralized optimization of multi-agent distribution networks considering uncertainties is essential to improving user-side energy utilization efficiency and flexibility, whereas a contradictory centralized coordinator aware of system-level information is inevitably introduced in existing researches. To address the problem, this paper introduces the flexibility boundaries of nodes to express their adjustability under uncertainties and constructs a flexibility transition model to express their neighbourhood relationship. Besides, a robust interval power flow model is established to consider the stochastic impact of generation on nodal voltage through neighbourhood information exchange. Based on the above two models, the robust optimization problem is established to minimize the baseline operation cost and maximize the allowable generation limits of non-dispatchable renewable energy sources. The model involves no global information and is solved with alternating direction method of multipliers(ADMM) in a fully decentralized way. Case study on a modified IEEE 33-bus and a 118-bus system is presented and the proposed method is compared with conventional multi-level robust formulations. The results suggest the effectiveness and correctness of the newly proposed method.

Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable., Comment: 11 pages, 10 figures

Data security and resource allocation are two important terms associated with Internet of Things (IoT). This recent technical evolution has made its mark in industrial applications making the network more flexible and computation friendly through connecting all the devices. As a subset of IoT, the framework of Industrial IoT (IIoT) is based on huge number of nodes with continuous process of multiple works at a time. Due to this, multi-objective network, interference in the path always becomes reason for the loss of network resources as well as the security of data becomes vulnerable. In most of the previous works, dedicated channel states are considered for fixed resources which remains a major issue of IIoT network flexibility along with security. In this paper, both the problems are incorporated by calculating the channel security and using convolutional neural network (CNN) optimal channel state extracted for different applications. This results as a fast system with proper utilization of resources and validated with mathematical analysis and simulations.

For a general production network, state-of-the-art methods for constructing sparse flexible designs are heuristic in nature, typically computing a proxy for the quality of unseen networks and using that estimate in a greedy manner to modify a current design. This paper develops two machine learning-based approaches to constructing sparse flexible designs that leverage a neural network to accurately and quickly predict the performance of large numbers of candidate designs. We demonstrate that our heuristics are competitive with existing approaches and produce high-quality solutions for both balanced and unbalanced networks. Finally, we introduce a novel application of process flexibility in healthcare operations to demonstrate the effectiveness of our approach in a large numerical case study. We study the flexibility of linear accelerators that deliver radiation to treat various types of cancer. We demonstrate how clinical constraints can be easily absorbed into the machine learning subroutine and how our sparse flexible treatment networks meet or beat the performance of those designed by state-of-the-art methods.

The use of de-icing salts and other anti-skid measures typically adopted by highway maintenance services have many limitations such as applicable temperatures, location of storage facilities, ease ...

Multimodal displays are a long-standing fascination of behavioural biologists because many signallers can adjust display architecture or switch signalling modality, often to exploit the advantages or avoid the disadvantages of a particular signalling environment. Yet, how such adaptive flexibility occurs remains poorly understood. Here, we argue that studying the endocrine modulation of multimodal signal production can close this knowledge gap. We first highlight a concept well known to behavioural endocrinologists that sex steroid hormones, and in particular androgens, can mediate the integration of multiple signalling traits at the organismal level. We then hypothesize that endocrine responses also play a role in maintaining flexibility in multimodal displays over time and space and may be key in responding quickly to fluctuating environments. To support these ideas, we use as our major example our own work in ‘foot-flagging’ frogs, Staurois parvus. We provide an overview of prior work on multimodal signalling in this species, as well as initial data from network analyses that point to androgen-mediated adaptive shifts in multimodal display architecture. We conclude by suggesting future work to further elucidate the connections between the signalling environment, androgenic hormones and behavioural flexibility in multimodal communication.

While traditional robotic systems come with a monolithic system design, reconfigurable multirobot systems can share and shift physical resources in an on-demand fashion. Multirobot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this article details an organization model as basis for planning with reconfigurable multirobot systems. The model allows us to exploit redundancies when optimizing a multirobot system’s probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multirobot missions. We use a simulated multirobot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape. Our implementation of the organization model is open-source available (https://github.com/rock-knowledge-reasoning/knowledge-reasoning-moreorg).

Carbon-based conductive and electroactive materials (e.g., derivatives of graphene, fullerenes, polypyrrole, polythiophene, polyaniline) have been studied since the 1970s for use in a broad range of applications. These materials have electrical properties comparable to those of commonly used metals, while providing other benefits such as flexibility in processing and modification with biologics (e.g., cells, biomolecules), to yield electroactive materials with biomimetic mechanical and chemical properties. In this review, we focus on the uses of these electroconductive materials in the context of the central and peripheral nervous system, specifically recent studies in the peripheral nerve, spinal cord, brain, eye, and ear. We also highlight in vivo studies and clinical trials, as well as a snapshot of emerging classes of electroconductive materials (e.g., biodegradable materials). We believe such specialized electrically conductive biomaterials will clinically impact the field of tissue regeneration in the foreseeable future. Statement of significance This review addresses the use of conductive and electroactive materials for neural tissue regeneration, which is of significant interest to a broad readership, and of particular relevance to the growing community of scientists, engineers and clinicians in academia and industry who develop novel medical devices for tissue engineering and regenerative medicine. The review covers the materials that may be employed (primarily focusing on derivatives of fullerenes, graphene and conjugated polymers) and techniques used to analyze materials composed thereof, followed by sections on the application of these materials to nervous tissues (i.e., peripheral nerve, spinal cord, brain, optical, and auditory tissues) throughout the body.

Negotiating parties to an environmental agreement can manage uncertainty by including flexibility clauses, such as escape and withdrawal clauses. This article investigates a type of uncertainty so far overlooked by the literature: the uncertainty generated by the creation of a Conference of the Parties (COP) in a context of sharp power asymmetry. When negotiating an agreement, it is difficult for powerful states to make a credible commitment to weaker states, whereby they will not abuse their power to influence future COP decision-making. Flexibility clauses provide a solution to this credibility issue. They act as an insurance mechanism in case a powerful state hijacks the COP. Thus we expect that the creation of a collective body interacts with the degree of power asymmetry to make flexibility clauses more likely in environmental agreements. To test this argument, we draw on an original data set of several specific clauses in 2,090 environmental agreements, signed between 1945 and 2018. The results support our hypothesis and suggest that flexibility clauses are an important design feature of adaptive environmental agreements.

Under the needs of processing huge amounts of data, providing high-quality service, and protecting user privacy in artificial intelligence of things (AIoT), federated learning (FL) has been treated as a promising technique to facilitate distributed learning with privacy protection. Although the importance of developing privacy-preserving FL has attracted a lot of attentions, the existing research only focuses on FL with independent identically distributed (i.i.d.) data and lacks study of non-i.i.d. scenario. What is worse, the assumption of i.i.d. data is impractical, reducing the performance of privacy protection in real applications. In this article, we carry out an innovative exploration of privacy protection in FL with non-i.i.d. data. First, a thorough analysis on privacy leakage in FL is conducted with proving the performance upper bound of privacy inference attack. Based on our analysis, a novel algorithm, 2DP-FL, is designed to achieve differential privacy by adding noise during training local models and when distributing global model. Especially, our 2DP-FL algorithm has a flexibility of noise addition to meet various needs and has a convergence upper bound. Finally, the real-data experiments can validate the results of our the oretical analysis and the advantages of 2DP-FL in privacy protection, learning convergence, and model accuracy.

Taxi business, with its ubiquitous availability, route flexibility and comfortable travel experience, offers a complementary service for the public transportation system. Among the existing methods of doing taxi business, an meaningful issue is to mine efficient seeking strategies for taxi drivers, in order to improve transportation efficiency. Recent efforts have been made mainly on the individual recommendation with respect to shorter seeking time and higher seeking efficiency, whereas the global transportation efficiency will greatly be reduced once each driver only pays attention to his local optimization. Rather than the individual recommendation, in this paper we conduct research on mining the taxis mobility from large-scale taxi data, thereby proposing a novel solution, namely GREEN (short for A Global RoutEs rEcommeNdation), to improve the seeking strategies and optimize the global transportation situation. Specifically, we first investigate how the drop-off information affects seeking strategies and conduct quantitive analysis, revealing the impact of seeking efficiency, passenger density and top drivers' experience. Moreover, to deal with the conflict between local optimization and global optimization, we dynamically adjust the weights of road segments based on the number of vacant taxis passing through each road segment. Also, to well evaluate the transportation efficiency, we define the seeking efficiency, net revenue and operation efficiency. Extensive experiments on the real-world dataset demonstrate that our scheme can work well, which not only improves the overall seeking efficiency by reducing total vacant driving time, but also increases the global operation efficiency, thereby optimizing the global transportation efficiency.

Automotive cyber-physical systems need to be rigorously checked and tested under various physical conditions. Automakers aim to improve development efficiency of the automotive cyber-physical systems in the fierce market competition. However, the actual development process suffers from the challenges of long development cycle and poor scalability. To tackle these challenges, this article develops a digital twinning based adaptive development environment for automotive cyber-physical systems, which addresses two critical problems: each physical entity (i.e., electronic control unit, component, test source, etc.) needs to clone a corresponding digital twin; digital twins and the physical entities need to interact closely. The first problem is addressed through proposing an integrated digital twinning clone flow. The second problem is addressed through developing a smart digital twinning board. Our case study with the automotive body control system demonstrates that the adaptive development environment achieves a high adaptability with short development cycle, low complexity, low cost, high scalability, and high flexibility, which meet various automotive cyber-physical design requirements during the development process.

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

A model-based signal processing framework is proposed for pandemic trend forecasting and control, by using non-pharmaceutical interventions (NPI) at regional and country levels worldwide. The control objective is to prescribe quantifiable NPI strategies at different levels of stringency, which balance between human factors (such as new cases and death rates) and cost of intervention per region/country. Due to infrastructural disparities and differences in priorities of regions and countries, strategists are given the flexibility to weight between different NPIs and to select the desired balance between the human factor and overall NPI cost. The proposed framework is based on a \textit{finite-horizon optimal control} (FHOC) formulation of the bi-objective problem and the FHOC is numerically solved by using an ad hoc \textit{extended Kalman filtering/smoothing} framework for optimal NPI estimation and pandemic trend forecasting. The algorithm enables strategists to select the desired balance between the human factor and NPI cost with a set of weights and parameters. The parameters of the model are partially selected by epidemiological facts from COVID-19 studies, and partially trained by using machine learning techniques. The developed algorithm is applied on ground truth data from the Oxford COVID-19 Government Response Tracker project, which has categorized and quantified the regional responses to the pandemic for more than 300 countries and regions worldwide, since January 2020. The dataset was used for NPI-based prediction and prescription during the XPRIZE Pandemic Response Challenge., 11 Pages, 6 figures, 24 references

The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties.

Recent proposals of deep learning-based beamformers for ultrasound imaging (US) have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be readily combined. Unfortunately, with the existing technology, a large number of beamformers need to be trained and stored for different probes, organs, depth ranges, operating frequency, and desired target 'styles', demanding significant resources such as training data, etc. To address this problem, here we propose a switchable and tunable deep beamformer that can switch between various types of outputs such as DAS, MVBF, DMAS, GCF, etc., and also adjust noise removal levels at the inference phase, by using a simple switch or tunable nozzle. This novel mechanism is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated using a single generator by merely changing the AdaIN codes. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed method for various applications.