Phase reconfiguration of heterogeneous CoFeS/CoNiS nanoparticles for superior battery-type supercapacitors

Developing advanced battery-type materials with abundant active sites,high conductivity,versatile morphologies,and hierarchically porous structures is crucial for realizing high-quality hybrid supercapacitors.Herein,heterogeneous FeS@NiS is synthesized by cationic Co doping via surface-structure engineering.The density functional theory(DFT)theoretical calculations are firstly performed to predict the advantages of Co dopant by improving the OH^(−)adsorption properties and adjusting electronic structure,benefiting ions/electron transfer.The dynamic surface evolution is further explored which demonstrates that CoFeS@CoNiS could be quickly reconstructed to Ni(Co)Fe_(2)O_(4)during the charging process,while the unstable structure of the amorphous Ni(Co)Fe_(2)O_(4)results in partial conversion to Ni/Co/FeOOH at high potentials,which contributes to the more reactive active site and good structural stability.Thus,the free-standing electrode reveals excellent electrochemical performance with a superior capacity(335.6 mA h g^(−1),2684 F g^(−1))at 3 A g^(−1).Furthermore,the as-fabricated device shows a quality energy density of 78.1 W h kg^(−1)at a power density of 750 W kg^(−1)and excellent cycle life of 92.1%capacitance retention after 5000 cycles.This work offers a facile strategy to construct versatile morphological structures using electrochemical activation and holds promising applications in energy-related fields.

Resilience-driven cooperative reconfiguration strategy for unmanned weapon system-of-systems

As the unmanned weap system-of systems(UWSoS)becomes complex,the inevitable uncertain interference gradu-ally increases,which leads to a strong emphasis on the resilience of UWSoS.Hence,this paper presents a resilience-driven cooperative reconfiguration strategy to enhance the resilience of UWSoS.First,a unified resilience-driven coopera-tive reconfiguration strategy framework is designed to guide the UWSoS resilience enhancement.Subsequently,a cooperative reconfiguration strategy algorithm is proposed to identify the optimal cooperative reconfiguration sequence,combining the cooperative pair resilience contribution index(CPRCI)and coop-erative pair importance index(CPII).At last,the effectiveness and superiority of the proposed algorithm are demonstrated through various attack scenario simulations that include differ-ent attack modes and intensities.The analysis results can pro-vide a reference for decision-makers to manage UWSoS.

A Critical Review of Active Distribution Network Reconfiguration:Concepts,Development,and Perspectives

In recent years,the large-scale grid connection of various distributed power sources has made the planning and operation of distribution grids increasingly complex.Consequently,a large number of active distribution network reconfiguration techniques have emerged to reduce system losses,improve system safety,and enhance power quality via switching switches to change the system topology while ensuring the radial structure of the network.While scholars have previously reviewed these methods,they all have obvious shortcomings,such as a lack of systematic integration of methods,vague classification,lack of constructive suggestions for future study,etc.Therefore,this paper attempts to provide a comprehensive and profound review of 52 methods and applications of active distribution network reconfiguration through systematic method classification and enumeration.Specifically,these methods are classified into five categories,i.e.,traditional methods,mathematical methods,meta-heuristic algorithms,machine learning methods,and hybrid methods.A thorough comparison of the various methods is also scored in terms of their practicality,complexity,number of switching actions,performance improvement,advantages,and disadvantages.Finally,four summaries and four future research prospects are presented.In summary,this paper aims to provide an up-to-date and well-rounded manual for subsequent researchers and scholars engaged in related fields.

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more complex.This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms.Consequently,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima.To tackle this issue,a more advanced particle swarm optimization algorithm is proposed.To address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning factors.The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization process.The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities.The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the results.Simulation analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy resources.The findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network.Furthermore,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply quality.Additionally,the algorithm exhibits a high level of generality and applicability.

Autonomous Control Reconfiguration of Aerospace Vehicle Based on Control Effectiveness Estimation

Future aerospace vehicles （ASV） are designed to fly in both inner and extra atmospheric fields, which requires autonomous adaptability to the uncertainties emanated from abrupt faults and continuously time-varying environments. An autonomous control reconfiguration scheme is presented for ASV to deal with the uncertainties on the base of control effectiveness estimation. The on-line estimation methods for the time-varying control effectiveness of linear control system are investigated. Some sufficient conditions for the estimable system are given for different cases. There are proposed corresponding on-line estimation algorithms which are proved to be convergent and robust to noise using the least-square-based methods. On the ground of fuzzy logic and linear programming, the control allocation algorithms, which are able to implement the autonomous control reconfiguration through the redundant actuators, are put forward. Finally, an integrated system is developed to verify the scheme and algorithms by way of numerical simulation and analysis.

Optimization method for evacuation zone with network reconfiguration based on dynamic simulation

Abstract： With a determinate danger zone and evacuation demand caused by an emergency, an optimization method for the evacuation zone with network reconfiguration based on dynamic simulation is proposed. The method contains three modules. First, the network in the evacuation zone is optimized by a model with the integrated strategy of lane reversal and intersection conflict elimination. Secondly, the dynamic evacuation simulation model based on the cell transmission model is applied to simulate the dynamic propagation process of evacuated vehicles in the network in the evacuation zone. The evacuation time for all evacuated vehicles leaving the danger zone is obtained and the setting of the current evacuation zone is fed back. Thirdly, the arrival distributions of evacuated vehicles at critical intersections of the evacuation zone are also obtained to estimate the delay at critical intersection to determine whether the intersection should be taken as the critical intersection in the next iteration. The evacuation zone is expanded gradually through iteration, and the reasonable evacuation zone and the optimal evacuation network is confirmed. Based on the survey of the parking lot and urban street network around Nanjing Olympic Sports Center, the models and the iterative algorithm were applied to obtain the optimal plan of the evacuation zone with network reconfiguration in an evacuation situation to verify the validity of the proposed method.

ADAPTIVE RECONFIGURATION CONTROL FOR FIGHTERS BASED ON WEIGHTED MULTIPLE-MODEL-STRUCTURE

Aimed at the complex flight control system of a fighter,a kind of robust adaptive control methods using multiple models is presented to make the control system track the given signal under different working conditions and to reconfigure the control law for some structural failures. Firstly,the multiple-model control structure is formed by several linear models and one fuzzy model. In the fuzzy logic way,weights of the multiple-model adaptive controller are obtained. Then,a dynamic structure adaptive neural network is introduced to stabilize the whole system and eliminate the influence caused by the frequent switching. Simulation results show that the control method is effective by demonstrating the normal flight process and the control simulation with failures.

Distribution Network Reconfiguration Based on ESA

An efficient algorithm ESA combining evolution strategies(ES) with simulated annealing(SA) is proposed in this paper. We first use ES to choose an initial temperature, then use a modified SA to find a global optimum for the problem. An efficient load flow method and a heuristic criterion for determining the temperature lowering scheme are employed in order to speed up the computation. The solution algorithm has been tested on a distribution system with very promising results.

Formation reconfiguration in restricted three body problem

Reconfiguration of formation flying around a halo orbit of the Sun-Earth restricted three body system is investigated with impulse maneuvers. For a short time reconfiguration, the two-impulse maneuver is investigated with both analytical and numerical methods and the Beginning-Ending （BE） method is proven to be an energy-optimal one of all two-impulse （TI） reconfigurations, and the energy consumption of BE is independent of the position of the chief spacecraft, and decreases with the reconfiguration time. Then, genetic algorithm is adopted to optimize the energy consumption. The results show that the optimal energy increases with radius difference between the initial and final orbits, and decreases with the reconfiguration time.

HRM: H-tree based reconfiguration mechanism in reconfigurable homogeneous PE array

In order to accommodate the variety of algorithms with different performance in specific application and improve power efficiency,reconfigurable architecture has become an effective methodology in academia and industry.However,existing architectures suffer from performance bottleneck due to slow updating of contexts and inadequate flexibility.This paper presents an H-tree based reconfiguration mechanism(HRM)with Huffman-coding-like and mask addressing method in a homogeneous processing element(PE)array,which supports both programmable and data-driven modes.The proposed HRM can transfer reconfiguration instructions/contexts to a particular PE or associated PEs simultaneously in one clock cycle in unicast,multicast and broadcast mode,and shut down the unnecessary PE/PEs according to the current configuration.To verify the correctness and efficiency,we implement it in RTL synthesis and FPGA prototype.Compared to prior works,the experiment results show that the HRM has improved the work frequency by an average of 23.4%,increased the updating speed by 2×,and reduced the area by 36.9%;HRM can also power off the unnecessary PEs which reduced 51%of dynamic power dissipation in certain application configuration.Furthermore,in the data-driven mode,the system frequency can reach 214 MHz,which is 1.68×higher compared with the programmable mode.

Multimodel-based flight control system reconfiguration control in the presence of input constraints

In this paper,an active fault accommodate strategy is proposed for the plant in the presence of actuator fault and input constraints,which is a combination of a direct adaptive control algorithm with multiple model switching.The μ-modification is introduced in the model reference architecture to construct the adaptive controller.The proof of stability is based on the candidate Lyapunov function,while appropriate switching of multiple models guarantees asymptotic tracking of the system states and the boundedness of all signals.Simulation results illustrate the efficiency of the proposed method.

Principle and architecture of parallel reconfiguration circuit for ternary optical computer

Reconfiguration is the key to produce an applicable ternary optical computer （TOC）. The method to implement the reconfiguration function determines whether a TOC can step into applied fields or not. In this work, a design of the reconfiguration circuit based on field programmable gates array （FPGA） is proposed, and the structure of the entire hardware system is discussed.

Path planning for UAVs formation reconfiguration based on Dubins trajectory

Multiple UAVs are usually deployed to provide robustness through redundancy and to accomplish surveillance,search,attack and rescue missions.Formation reconfiguration was inevitable during the flight when the mission was adjusted or the environment varied.Taking the typical formation reconfiguration from a triangular penetrating formation to a circular tracking formation for example,a path planning method based on Dubins trajectory and particle swarm optimization(PSO)algorithm is presented in this paper.The mathematic model of multiple UAVs formation reconfiguration was built firstly.According to the kinematic model of aerial vehicles,a process of dimensionality reduction was carried out to simplify the model based on Dubins trajectory.The PSO algorithm was adopted to resolve the optimization problem of formation reconfiguration path planning.Finally,the simulation and vehicles flight experiment are executed.Results show that the path planning method based on the Dubins trajectory and the PSO algorithm can generate feasible paths for vehicles on time,to guarantee the rapidity and effectiveness of formation reconfigurations.Furthermore,from the simulation results,the method is universal and could be extended easily to the path planning problem for different kinds of formation reconfigurations.

Adaptor Reconfiguration Analysis in Web Services Composition

Most of the existing approaches focus on identifying mismatches and synthesizing adaptors at design-time or recently at run-time. However, few works have been proposed to support adaptor reconfiguration when services in the composition evolve due to changes in business needs. To address the deficiencies, the problem of adaptor reconfiguration is targeted in the context of service composition. Firstly, the formal models for describing services and adaptors are presented. Then, under this formalization,the notion of reconfiguration compliance is proposed to determine the validity of an adaptor instance with respect to its history executions and future executions. Based on the notion,the algorithm for reconfiguration analysis of adaptors is presented and it can be used for determining the migratability of an adaptor instance and the corresponding target state of reconfiguration if migratable.Finally,feasibility of the proposed approach is validated on a realistic case study. The proposed approach improves the flexibility of adaptor-based service composition by equipping adaptors with reconfiguration capabilities.

Multi-agent System Optimized Reconfiguration of Shipboard Power System

Reconfigurability of the electrical network in a shipboard power system （SPS） after its failure is central to the restoration of power supply and improves survivability of an SPS. The navigational process creates a sequence of different operating conditions. The priority of some loads differs in changing operating conditions. After analyzing characteristics of typical SPS, a model was developed used a grade III switchboard and an environmental prioritizing agent （EPA） algorithm. This algorithm was chosen as it is logically and physically decentralized as well as multi-agent oriented. The EPA algorithm was used to decide on the dynamic load priority, then it selected the means to best meet the maximum power supply load. The simulation results showed that higher priority loads were the first to be restored. The system satisfied all necessary constraints, demonstrating the effectiveness and validity of the proposed method.

A compound objective reconfiguration of distribution networks using hierarchical encoded particle swarm optimization

With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.

Management and Reconfiguration of a Radiology Department under the Threat of Coronavirus Disease 2019:Experience from Wuhan

Summary:The corona virus disease 2019(COVID-19)is an emerging respiratory infectious disease caused by SARS-CoV-2,which first occurred in December 2019 in Wuhan,China.These days,in China,chest CT is used for diagnosis of COVID-19,as an important complement to the reverse-transcription polymerase chain reaction(RT-PCR)test.Because of contacting with a large number of suspected or probable cases closely during chest CT examination,radiographers are easily infected with COVID-19.This article included the rearrangement of CT examination room in fever clinic,the rearrangement of human resources in radiology department,and the drafting of new operating procedures for radiologists who carry out CT examination on COVID-19 patients.This article also introduced the emergency management procedures of the department of radiology during the outbreak,and the experience of infection prevention for the staff of the department of radiology.

Probabilistic Fuzzy Approach to Assess RDS Vulnerability and Plan Corrective Action Using Feeder Reconfiguration

Two common problems for a typical Power distribution system are voltage collapse & instability. Challenge is to identify the vulnerable nodes and apply the effective corrective actions. This paper presents a probabilistic fuzzy approach to assess the node status and proposes feeder reconfiguration as a method to address the same. Feeder reconfiguration is altering the topological structures of distribution feeders by changing the open/closed states of the sectionalizing and ties switches. The solution is converge using a probabilistic fuzzy modeled solution, which defines the nodal vulnerability index (VI) as a function of node voltage and node voltage stability index and predicts nodes critical to voltage collapse. The information is further used to plan best combination of feeders from each loop in distribution system to be switched out such that the resulting configuration gives the optimal performance i.e. best voltage profile and minimal kW losses. The proposed method is tested on established radial distribution system and results are presented.

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

Constellation reconfiguration is a critical issue to recover from the satellite failure,maintain the regular operation,and enhance the overall performance.The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size.To solve such real-world problems that can be hardly solved by traditional algorithms,the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space.An adaptive innovationdriven multi-objective evolutionary algorithm(MOEA-AI)employing automated innovation(AI)and adaptive operator selection(AOS)is proposed to extract and apply domain knowledge.The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism.To prevent the overuse of knowledgedependent operators,AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators.It evaluates and selects operators according to their actual performance,which helps to identify useful operators from the candidate set.The efficacy of the MOEAAI framework is demonstrated by the simulation of emergency missions.It was verified that the proposed algorithm can discover a non-dominant solution set with better quality,more homogeneous distribution,and better adaptation to practical situations.

Multi-agent and ant colony optimization for ship integrated power system network reconfiguration

Electric power is widely used as the main energy source of ship integrated power system(SIPS), which contains power network and electric power network. SIPS network reconfiguration is a non-linear large-scale problem. The reconfiguration solution influences the safety and stable operation of the power system. According to the operational characteristics of SIPS, a simplified model of power network and a mathematical model for network reconfiguration are established. Based on these models, a multi-agent and ant colony optimization(MAACO) is proposed to solve the problem of network reconfiguration. The simulations are carried out to demonstrate that the optimization method can reconstruct the integrated power system network accurately and efficiently.