Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

The CCD instrument background of the SMILE SXI

The ESA and CAS SMILE mission orbit is highly elliptical and will pass through multiple radiation environments.The Soft X-ray Imager(SXI)instrument aboard has a radiation shutter door designed to close when the surrounding radiation flux is high.The shutter door will close when passing below an altitude threshold to protect against trapped particles in the Earth’s Van Allen Belts.Therefore,two radiation environments can be approximated based on the shutter door position:open and closed.The instrument background for the CCDs(Charge-Coupled Devices)that form the focal plane array of the SXI were evaluated for the two environments.Due to the correlation of the space environment with the solar cycle,the solar minima and maxima,the background was also evaluated at these two extremes.The results demonstrated that the highest instrument background will occur during solar minima due to the main contributing source being Galactic Cosmic Rays(GCRs).It was also found that the open background was highest for solar minima and that the closed background was highest during solar maxima.This is due to the radiation shutter door acting as a scattering centre and the changes in the energy flux distribution of the GCRs between the two solar extremes.

Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore,a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy(CARSS)Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.TheMAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net(TAPN)is used to model system formally and behaviour is expressed in TCTL,while TAPAAL model checker verifies the underline properties of the system.

Self-adaptive bulk/surface engineering of Bi_(x)O_(y)Br_(z) towards enhanced photocatalysis:Current status and future challenges

The bulk/surface states of semiconductor photocatalysts are imperative parameters to maneuver their performance by significantly affecting the key processes of photocatalysis including light absorption,separation of charge carrier,and surface site reaction.Recent years have witnessed the encouraging progress of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z) for photocatalytic applications spanning various fields.However,despite the maturity of current research,the interaction between the bulk/surface state and the performance of Bi_(x)O_(y)Br_(z) has not yet been fully understood and highlighted.In this regard,a timely tutorial overview is quite urgent to summarize the most recent key progress and outline developing obstacles in this exciting area.Herein,the structural characteristics and fundamental principles of Bi_(x)O_(y)Br_(z)for driving photocatalytic reaction as well as related key issues are firstly reviewed.Then,we for the first time summarized different self-adaptive engineering processes over Bi_(x)O_(y)Br_(z)followed by a classification of the generation approaches towards diverse Bi_(x)O_(y)Br_(z)materials.The features of different strategies,the up-to-date characterization techniques to detect bulk/surface states,and the effect of bulk/surface states on improving the photoactivity of Bi_(x)O_(y)Br_(z)in expanded applications are further discussed.Finally,the present research status,challenges,and future research opportunities of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z)are prospected.It is anticipated that this critical review can trigger deeper investigations and attract upcoming innovative ideas on the rational design of Bi_(x)O_(y)Br_(z)-based photocatalysts.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

STABILITY OF TRANSONIC SHOCKS TO THE EULER-POISSON SYSTEM WITH VARYING BACKGROUND CHARGES

This paper is devoted to studying the stability of transonic shock solutions to the Euler-Poisson system in a one-dimensional nozzle of finite length.The background charge in the Poisson equation is a piecewise constant function.The structural stability of the steady transonic shock solution is obtained by the monotonicity argument.Furthermore,this transonic shock is proved to be dynamically and exponentially stable with respect to small perturbations of the initial data.One of the crucial ingredients of the analysis is to establish the global well-posedness of a free boundary problem for a quasilinear second order equation with nonlinear boundary conditions.

In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li dendrites.Herein,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state batteries.Specifically,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion transport.While the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li deposition.As expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical passivation.For Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 cycles.Additionally,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical performance.The present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

Characteristics of Lightning Activity in Southeast China and its Relation to the Atmospheric Background

Based on the lightning observation data from the Fengyun-4A(FY-4A)Lightning Mapping Imager(FY-4A/LMI)and the Lightning Imaging Sensor(LIS)on the International Space Station(ISS),we extract the“event”type data as the lightning detection results.These observations are then compared with the cloud-to-ground(CG)lightning observation data from the China Meteorological Administration.This study focuses on the characteristics of lightning activity in Southeast China,primarily in Jiangxi Province and its adjacent areas,from April to September,2017–2022.In addition,with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis data,we further delved into the potential factors influencing the distribution and variations in lightning activity and their primary related factors.Our findings indicate that the lightning frequency and density of the FY-4A/LMI,ISS-LIS and CG data are higher in southern and central Jiangxi,central Fujian Province,and western and central Guangdong Province,while they tend to be lower in eastern Hunan Province.In general,the high-value areas of lightning density for the FY-4A/LMI are located in inland mountainous areas.The lower the latitude is,the higher the CG lightning density is.High-value areas of the CG lightning density are more likely to be located in eastern Fujian and southeastern Zhejiang Province.However,the high-value areas of lightning density for the ISS-LIS are more dispersed,with a scattered distribution in inland mountainous areas and along the coast of eastern Fujian.Thus,the mountainous terrain is closely related to the high-value areas of the lightning density.The locations of the high-value areas of the lightning density for the FY-4A/LMI correspond well with those for the CG observations,and the seasonal variations are also consistent.In contrast,the distribution of the high-value areas of the lightning density for the ISS-LIS is more dispersed.The positions of the peak frequency of the FY-4A/LMI lightning and CG lightning contrast with local altitudes,primarily located at lower altitudes or near mountainsides.K-index and convective available potential energy(CAPE)can better reflect the local boundary layer conditions,where the lightning density is higher and lightning seasonal variations are apparent.There are strong correlations in the annual variations between the dew-point temperature(Td)and CG lightning frequency,and the monthly variations of the dew-point temperature and CAPE are also strongly correlated with monthly variations of CG lightning,while they are weakly correlated with the lightning frequency for the FY-4A/LMI and ISS-LIS.This result reflects that the CAPE shows a remarkable effect on the CG lightning frequency during seasonal transitions.

Gravitational Waves Background, as Well as Some UFO, FRB and Supernova Flares, Are Due to Compressibility of the Spacetime (CoST)

The recently observed gravitational wave background is explained in terms of the quantum modification of the general relativity (Qmoger). Some UFO, FRB and supernova flares also can be explained in terms of Qmoger.

Comparative Analysis of Energy Characteristics of Two Southwest Vortices in Sichuan Under Similar Circulation Backgrounds

Based on ERA5 reanalysis data,the present study analyzed the thermal energy development mechanism and kinetic energy conversion characteristics of two extreme rainstorm processes in relation to the shallow southwest vortex in the warm-sector during a“rain-generated vortex”process and the deep southwest vortex in a“vortex-generated rain”process.The findings were as follows:(1)During the extreme rainstorm on August 11,2020(hereinafter referred to as the“8·11”process),intense surface heating and a high-energy unstable environment were observed.The mesoscale convergence system triggered convection to produce heavy rainfall,and the release of latent condensation heat generated by the rainfall promoted the formation of a southwest vortex.The significant increase(decrease)in atmospheric diabatic heating and kinetic energy preceded the increase(decrease)in vorticity.By contrast,the extreme rainstorm on August 16,2020(hereinafter referred to as the“8·16”process)involved the generation of southwest vortex in a low-energy and highhumidity environment.The dynamic uplift of the southwest vortex triggered rainfall,and the release of condensation latent heat from rainfall further strengthened the development of the southwest vortex.The significant increase(decrease)in atmospheric diabatic heating and kinetic energy exhibited a delayed progression compared to the increase(decrease)in vorticity.(2)The heating effect around the southwest vortex region was non-uniform,and the heating intensity varied in different stages.In the“8·11”process,the heating effect was the strongest in the initial stage,but weakened during the vortex's development.On the contrary,the heating effect was initially weak in the“8·16”process,and intensified during the development stage.(3)The available potential energy of the“8·11”process significantly increased in kinetic energy converted from rotational and divergent winds through baroclinic action,and the divergent wind energy continued to convert into rotational wind energy.By contrast,the“8·16”process involved the conversion of rotational wind energy into divergent wind energy,which in turn converted kinetic energy back into available potential energy,thereby impeding the further development and maintenance of the southwest vortex.

Optimal Insurance with Background Risk under the Ambiguity and Belief Heterogeneity Structure

In this paper, we discuss the optimal insurance in the presence of background risk while the insured is ambiguity averse and there exists belief heterogeneity between the insured and the insurer. We give the optimal insurance contract when maxing the insured’s expected utility of his/her remaining wealth under the smooth ambiguity model and the heterogeneous belief form satisfying the MHR condition. We calculate the insurance premium by using generalized Wang’s premium and also introduce a series of stochastic orders proposed by [1] to describe the relationships among the insurable risk, background risk and ambiguity parameter. We obtain the deductible insurance is the optimal insurance while they meet specific dependence structures.

Ratio of Gravitational Force to Electric Force from Empirical Equations in Terms of the Cosmic Microwave Background Temperature

Previously, we presented several empirical equations using the cosmic microwave background (CMB) temperature. Next, we propose an empirical equation for the fine-structure constant. Considering the compatibility among these empirical equations, the CMB temperature (Tc) and gravitational constant (G) were calculated to be 2.726312 K and 6.673778 × 10−11 m3∙kg−1∙s−2, respectively. Every equation could be explained in terms of the Compton length of an electron (λe), the Compton length of a proton (λp) and a. Furthermore, every equation could also be explained in terms of Avogadro’s number and the number of electrons in 1 C. However, the ratio of the gravitational force to the electric force cannot be uniquely determined when the unit of the Planck constant (Js) is changed. In this study, we showed that every equation can be described in terms of Planck constant. From the assumption of minimum mass, the ratio of gravitational force to electric force could be elucidated.

Correspondence Principle for Empirical Equations in Terms of the Cosmic Microwave Background Temperature with Solid-State Ionics

Previously, we presented several empirical equations using the cosmic microwave background (CMB) temperature that were mathematically connected. Next, we proposed an empirical equation for the fine-structure constant. Considering the compatibility among these empirical equations, the CMB temperature (Tc) and gravitational constant (G) were calculated to be 2.726312 K and 6.673778 × 10-11 m3·kg-1·s-2, respectively. Every equation can be explained in terms of the Compton length of an electron (λe), the Compton length of a proton (λp) and α. However, these equations are difficult to follow. Using the correspondence principle with the thermodynamic principles in solid-state ionics, we propose a canonical ensemble to explain these equations in this report. For this purpose, we show that every equation can be explained in terms of Avogadro’s number and the number of electrons in 1 C.

Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem

Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

Large eddy simulation of aircraft wake vortex with self-adaptive grid method

A self-adaptive-grid method is applied to numerical simulation of the evolu- tion of aircraft wake vortex with the large eddy simulation （LES）. The Idaho Falls （IDF） measurement of run 9 case is simulated numerically and compared with that of the field experimental data. The comparison shows that the method is reliable in the complex atmospheric environment with crosswind and ground effect. In addition, six cases with different ambient atmospheric turbulences and Brunt V^iis/il^i （BV） frequencies are com- puted with the LES. The main characteristics of vortex are appropriately simulated by the current method. The onset time of rapid decay and the descending of vortices are in agreement with the previous measurements and the numerical prediction. Also, sec-ondary structures such as baroclinic vorticity and helical structures are also simulated. Only approximately 6 million grid points are needed in computation with the present method, while the number can be as large as 34 million when using a uniform mesh with the same core resolution. The self-adaptive-grid method is proved to be practical in the numerical research of aircraft wake vortex.

Self-adaptive one-dimensional nonlinear finite element method based on element energy projection method

The element energy projection （EEP） method for computation of super- convergent resulting in a one-dimensional finite element method （FEM） is successfully used to self-adaptive FEM analysis of various linear problems, based on which this paper presents a substantial extension of the whole set of technology to nonlinear problems. The main idea behind the technology transfer from linear analysis to nonlinear analysis is to use Newton＇s method to linearize nonlinear problems into a series of linear problems so that the EEP formulation and the corresponding adaptive strategy can be directly used without the need for specific super-convergence formulation for nonlinear FEM. As a re- sult, a unified and general self-adaptive algorithm for nonlinear FEM analysis is formed. The proposed algorithm is found to be able to produce satisfactory finite element results with accuracy satisfying the user-preset error tolerances by maximum norm anywhere on the mesh. Taking the nonlinear ordinary differential equation （ODE） of second-order as the model problem, this paper describes the related fundamental idea, the imple- mentation strategy, and the computational algorithm. Representative numerical exam- ples are given to show the efficiency, stability, versatility, and reliability of the proposed approach.

An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

An improved ensemble empirical mode decomposition(EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully.

Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm

A self-adaptive differential evolution neutron spectrum unfolding algorithm(SDENUA)is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer(WMNS).Specifically,the neutron fluence bounds are estimated to accelerate the algorithm convergence,and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10^(-6)to avoid unnecessary calculations.Furthermore,the crossover probability and scaling factor are self-adaptively controlled.FLUKA Monte Carlo is used to simulate the readings of the WMNS under(1)a spectrum of Cf-252 and(2)its spectrum after being moderated,(3)a spectrum used for boron neutron capture therapy,and(4)a reactor spectrum.Subsequently,the measured neutron counts are unfolded using the SDENUA.The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA,which does not require complex parameter tuning or an a priori default spectrum.The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1,and the errors of the final results calculated using the SDENUA are less than 12%.The established SDENUA can be used to unfold spectra from the WMNS.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.