STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

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.

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.

One-step Eco-friendly Fabrication of Antibacterial Polyester Via On-line Amination Reaction by Melt Coextrusion

The work is dedicated to develop a one-step eco-friendly method to prepare antibacterial polyethylene terephthalate(PET).We report a one-step eco-friendly method to manufacture antibacterial PET via on-line amination reaction by melt coextrusion.Beside evenly mixing of poly(hexamethylene guanidine)(PHMG)and PET in the melt coextrusion procedure,the amination reaction also occurred between PHMG and PET under high temperature(230-270℃).The antibacterial ability of composite PET showed obvious PHMG concentration dependence,and antibacterial activity reached more than 99%when PHMG content was 2.5 wt%.Moreover,LIVE/DEAD fluorescence test further confirmed that the composite PET could kill bacteria quickly and efiectively(within 30 min);while negligible cytotoxicity was observed to HSF and HUVEC cells.Onestep eco-friendly fabrication of composite antibacterial PET was accomplished by on-line melt coextrusion.The composite antibacterial PET has potential use in multiple fields to combat with pathogenic including textiles,packaging materials,decoration materials and biomedical devices,etc.

Flexible path planning-based reconfiguration strategy for maximum capacity utilization of battery pack

Maximizing the utilization of lithium-ion battery capacity is an important means to alleviate the range anxiety of electric vehicles.Battery pack inconsistency is the main limiting factor for improving battery pack capacity utilization,and poses major safety hazards to energy storage systems.To solve this problem,a maximum capacity utilization scheme based on a path planning algorithm is proposed.Specifically,the reconfigurable topology proposed is highly flexible and fault-tolerant,enabling battery pack consistency through alternating cell discharge and reducing the increased risk of short circuits due to relay error.The Dijkstra algorithm is used to find the optimal energy path,which can effectively remove faulty cells and find the current path with the best consistency of the battery pack and the lowest relay loss.Finally,the effectiveness of the scheme is verified by hardware-in-the-loop experiments,and the experimental results show that the state-of-charge SOC consistency of the battery pack at the end of discharge is increased by 34.18%,the relay energy loss is reduced by 0.16%,and the fault unit is effectively isolated.

Process reconfiguration and intensification:An emerging opportunity enabling efficient carbon capture and low-cost blue hydrogen production

Low-carbon hydrogen can play a significant role in decarbonizing the world. Hydrogen is currently mainly produced from fossil sources,requiring additional CO_(2)capture to decarbonize, which energy intense and costly. In a recent Green Energy & Environment paper, Cheng and Di et al. proposed a novel integration process referred to as SECLR_(HC) to generate high-purity H_(2) by in-situ separation of H_(2)and CO without using any additional separation unit. Theoretically, the proposed process can essentially achieve the separation of C and H in gaseous fuel via a reconfigured reaction process, and thus attaining high-purity hydrogen of ~99%, as well as good carbon and hydrogen utilization rates and economic feasibility. It displays an optimistic prospect that industrial decarbonization is not necessarily expensive, as long as a suitable CCS measure can be integrated into the industrial manufacturing process.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Reconfigurable and polarization-dependent optical filtering for transflective full-color generation utilizing low-loss phase-change materials

All-dielectric metasurface, which features low optical absorptance and high resolution, is becoming a promising candidate for full-color generation. However, the optical response of current metamaterials is fixed and lacks active tuning. In this work, we demonstrate a reconfigurable and polarization-dependent active color generation technique by incorporating low-loss phase change materials(PCMs) and CaF_2 all-dielectric substrate. Based on the strong Mie resonance effect and low optical absorption structure, a transflective, full-color with high color purity and gamut value is achieved. The spectrum can be dynamically manipulated by changing either the polarization of incident light or the PCM state. High transmittance and reflectance can be simultaneously achieved by using low-loss PCMs and substrate. The novel active metasurfaces can bring new inspiration in the areas of optical encryption, anti-counterfeiting, and display technologies.

Wideband low-scattering metasurface with an in-band reconfigurable transparent window

Active metasurfaces with dynamically reconfigurable functionalities are highly demanded in various practical applications.Here,we propose a wideband low-scattering metasurface that can realize an in-band reconfigurable transparent window by altering the operation states of the PIN diodes loaded on the structures.The metasurface is composed of a band-pass frequency selective surface(FSS)sandwiched between two polarization conversion metasurfaces(PCMs).PIN diodes are integrated into the FSS to switch the transparent window,while a checkerboard configuration is applied in PCMs for the diffusive-reflective function.A sample with 20×20 elements is designed,fabricated,and experimentally verified.Both simulated and measured results show that the in-band functions can be dynamically switched between beam-splitting scattering and high transmission by controlling the biasing states of the diodes,while low backscattering can be attained outside the passband.Furthermore,the resonant structures of FSS also play the role of feeding lines,thus significantly eliminating extra interference compared with conventional feeding networks.We envision that the proposed metasurface may provide new possibilities for the development of an intelligent stealth platform and its antenna applications.

Fixed-Time Sliding Mode Control With Varying Exponent Coefficient for Modular Reconfigurable Flight Arrays

The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.

Channel Estimation for Reconfigurable Intelligent Surface Aided Multiuser Millimeter-Wave/THz Systems

It is assumed that reconfigurable intelligent surface(RIS)is a key technology to enable the potential of mmWave communications.The passivity of the RIS makes channel estimation difficult because the channel can only be measured at the transceiver and not at the RIS.In this paper,we propose a novel separate channel estimator via exploiting the cascaded sparsity in the continuously valued angular domain of the cascaded channel for the RIS-enabled millimeter-wave/Tera-Hz systems,i.e.,the two-stage estimation method where the cascaded channel is separated into the base station(BS)-RIS and the RIS-user(UE)ones.Specifically,we first reveal the cascaded sparsity,i.e.,the sparsity exists in the hybrid angular domains of BS-RIS and the RIS-UEs separated channels,to construct the specific sparsity structure for RIS enabled multi-user systems.Then,we formulate the channel estimation problem using atomic norm minimization(ANM)to enhance the proposed sparsity structure in the continuous angular domains,where a low-complexity channel estimator via Alternating Direction Method of Multipliers(ADMM)is proposed.Simulation findings demonstrate that the proposed channel estimator outperforms the current state-of-the-arts in terms of performance.

Average Secrecy Capacity of the Reconfigurable Intelligent Surface-Assisted Integrated Satellite Unmanned Aerial Vehicle Relay Networks

Integrated satellite unmanned aerial vehicle relay networks(ISUAVRNs)have become a prominent topic in recent years.This paper investigates the average secrecy capacity(ASC)for reconfigurable intelligent surface(RIS)-enabled ISUAVRNs.Especially,an eve is considered to intercept the legitimate information from the considered secrecy system.Besides,we get detailed expressions for the ASC of the regarded secrecy system with the aid of the reconfigurable intelligent.Furthermore,to gain insightful results of the major parameters on the ASC in high signalto-noise ratio regime,the approximate investigations are further gotten,which give an efficient method to value the secrecy analysis.At last,some representative computer results are obtained to prove the theoretical findings.

Research and design of matrix operation accelerator based on reconfigurable array

In the case of massive data,matrix operations are very computationally intensive,and the memory limitation in standalone mode leads to the system inefficiencies.At the same time,it is difficult for matrix operations to achieve flexible switching between different requirements when implemented in hardware.To address this problem,this paper proposes a matrix operation accelerator based on reconfigurable arrays in the context of the application of recommender systems(RS).Based on the reconfigurable array processor(APR-16)with reconfiguration,a parallelized design of matrix operations on processing element(PE)array is realized with flexibility.The experimental results show that,compared with the proposed central processing unit(CPU)and graphics processing unit(GPU)hybrid implementation matrix multiplication framework,the energy efficiency ratio of the accelerator proposed in this paper is improved by about 35×.Compared with blocked alternating least squares(BALS),its the energy efficiency ratio has been accelerated by about 1×,and the switching of matrix factorization(MF)schemes suitable for different sparsity can be realized.

Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.

On-line血液透析滤过对移植肾功能延迟恢复的影响

目的 研究On -line血液透析滤过 (HDF)在移植肾功能延迟恢复患者中的应用。方法 我院2 0 0 2年 1月至 2 0 0 3年 7月肾移植术后发生移植肾功能延迟恢复患者 38例 ,2 9例采用Freseniuson -lineHDF透析机 ,F6 0透析器 ,透析滤过时间 3～ 4h ,血流量在 180～ 2 5 0ml/min ,置换液采取前稀释法 ,9例行HD治疗 ,采用GambroAK2 0 0透析机 ,F7透析器。结果 38例移植肾功能延迟恢复患者 ,2 9例用联机血液透析滤过 ,平均肾功能恢复时间为 ( 11.2± 5 .6 )天 ,治疗期间收缩压变化在 110～ 170mmHg,透析期间低血压发生率为 2 .6 % ,9例HD治疗者平均肾功能恢复时间为 ( 2 2 .3± 15 .7)天 ,透析期间低血压发生率为 13.2 %。结论 与HD比较 ,On -lineHDF对血压影响较小 ,可缩短移植肾功能恢复的时间。

平行机的实时到达on-line算法下界的改进

本文考虑了平行机实时到达的在线问题．模型中，工件是陆续到达的，工件的个数、到达时间是事先未知的，而且只有当工件到达，才知其加工时间，目标是使所有工件都加工完的时间达到最小．Ｃｈｅｎ与Ｖｅｓｔｊｅｎｓ（１９９６年）证明了该在线问题不存在性能比小于１．３４７３的ｏｎ－ｌｉｎｅ算法．本文将此界改进为（５－５）／２．