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.

Local dimming原理及在车载中的应用

Local dimming(局部调光)是一种低成本提升车载显示器对比度和清晰度的技术。在目前OLED技术尚无法突破严苛的汽车环境要求的状况下,局部调光是一种首选的成本和性能兼顾的技术。在满足汽车环境要求的同时,大幅提升了显示器的清晰度,提高了显示内容的辨识度,同时成本的上升也在合理范围内。它能给客户带来更好的体验,使驾驶更为安全。本文对它的工作原理以及如何应用进行了阐述,给读者提供了一种思路和方法来提升车载显示器的对比度。

儿童青少年使用DIMS镜片联合视觉训练控制近视效果研究

以5~18岁的近视儿童为研究对象,结合接受视觉训练意愿,非随机地分为多区正向光学离焦(DIMS)镜片组和DIMS镜片联合视觉训练组(联合组)观察近视控制的临床效果。对入组儿童的230只眼6个月随访的近视进展数据进行回顾性分析研究,发现所有入组儿童呈现缓慢的近视进展,平均屈光度进展量为(-0.07±0.18)D/半年,平均眼轴增长为(0.04±0.15)mm/半年;2个治疗组儿童的等效球镜度进展程度的分布没有统计学差异(X^(2)=3.200,P>0.05),眼轴进展程度的分布有统计学差异(X^(2)=10.667,P<0.05)。通过6个月的临床随访发现,近视儿童配戴DIMS镜片可以获得良好的屈光度和眼轴进展的控制效果,若同时联合进行视觉训练,对控制眼轴进展有更大帮助。

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.

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.

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.

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.

Self-adaptive strategy for one-dimensional finite element method based on EEP method with optimal super-convergence order

Based on the newly-developed element energy projection （EEP） method with optimal super-convergence order for computation of super-convergent results, an improved self-adaptive strategy for one-dimensional finite element method （FEM） is proposed. In the strategy, a posteriori errors are estimated by comparing FEM solutions to EEP super-convergent solutions with optimal order of super-convergence, meshes are refined by using the error-averaging method. Quasi-FEM solutions are used to replace the true FEM solutions in the adaptive process. This strategy has been found to be simple, clear, efficient and reliable. For most problems, only one adaptive step is needed to produce the required FEM solutions which pointwise satisfy the user specified error tolerances in the max-norm. Taking the elliptical ordinary differential equation of the second order as the model problem, this paper describes the fundamental idea, implementation strategy and computational algorithm and representative numerical examples are given to show the effectiveness and reliability of the proposed approach.

SELF-ADAPTIVE STRATEGY FOR ONE-DIMENSIONAL FINITE ELEMENT METHOD BASED ON ELEMENT ENERGY PROJECTION METHOD

Based on the newly-developed element energy projection （EEP） method for computation of super-convergent results in one-dimensional finite element method （FEM）, the task of self-adaptive FEM analysis was converted into the task of adaptive piecewise polynomial interpolation. As a result, a satisfactory FEM mesh can be obtained, and further FEM analysis on this mesh would immediately produce an FEM solution which usually satisfies the user specified error tolerance. Even though the error tolerance was not completely satisfied, one or two steps of further local refinements would be sufficient. This strategy was found to be very simple, rapid, cheap and efficient. Taking the elliptical ordinary differential equation of second order as the model problem, the fundamental idea, implementation strategy and detailed algorithm are described. Representative numerical examples are given to show the effectiveness and reliability of the proposed approach.

Local dimming高亮节能恒流控制算法的研究与应用

介绍了local dimming(区域调光)技术对LED背光亮度及整机功率的影响,通过实验数据分析了PWM(脉冲宽度调制)调光控制方式和DC(直流)调光控制方式引起的亮度变化、功率损耗、器件温升等问题,并结合实际案例给出了具体的改善对策。通过实验及大批量的生产验证表明,提出的具有创新性的恒流动态幅值控制算法对LED背光亮度、整机能效、器件温升等都有极大改善,对local dimming恒流控制方案的设计具有重要的借鉴和参考意义。

A Novel Sensing Imaging Equipment Under Extremely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremelydim(less than 0.001 Lux)environment.Based on a novel endoscopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aperture is designed.Combined with a water-air dual cooling intelligent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribution model,an optimal installation position and posture configuration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.

Dim-YOLOv5n昏暗场景目标检测算法

相比于正常光照场景,照明不良昏暗场景干扰因素较多,图像处理较为复杂,且现有的昏暗目标检测,存在参数量大,识别准确率低等不足。针对昏暗场景下目标检测算法中存在误检与漏检等问题,提出以YOLOv5n算法为基础进行改进的昏暗场景目标检测算法Dim-YOLOv5n。利用嵌入全维动态卷积(omni-dimensional dynamic convolution,ODConv)的轻量化主干ODConv-MobileNetV2替换主干网络,在减少计算量的同时提高检测精度。基于RepGFPN(reparameterized generalized-FPN)方法设计更加轻量高效的LigGFPN(lightweight generalized-FPN)加强特征融合网络,以提高网络特征提取能力,并在此基础上,使用GhostConv(ghost convolution)替换传统卷积,以减少模型的参数量。实验结果表明,改进后算法与原算法相比,检测精度P和召回率R分别提高了5.3个百分点和5个百分点,平均精度均值mAP0.5:0.95和mAP0.5分别提升了8.2个百分点和4.6个百分点,改进的算法在保证模型较小的同时有效提高了检测准确率。

Numerical simulation of LWD resistivity response of carbonate formation using self-adaptive hp-FEM

Most of the carbonate formation are highly heterogeneous with cavities of different sizes, which makes the prediction of cavity-filled reservoir in carbonate rocks difficult. Large cavities in carbonate formations pose serious threat to drilling operations. Logging-whiledrilling （LWD） is currently used to accurately identify and evaluate cavities in reservoirs during drilling. In this study, we use the self-adaptive hp-FEM algorithm simulate and calculate the LWD resistivity responses of fracture-cavity reservoir cavities. Compared with the traditional h-FEM method, the self-adaptive hp-FEM algorithm has the characteristics of the self-adaptive mesh refinement and the calculations exponentially converge to highly accurate solutions. Using numerical simulations, we investigated the effect of the cavity size, distance between cavity and borehole, and transmitted frequency on the LWD resistivity response. Based on the results, a method for recognizing cavities is proposed. This research can provide the theoretical basis for the accurate identification and quantitative evaluation of various carbonate reservoirs with cavities encountered in practice.

EVOLUTIONARY FUZZY GUIDANCE LAW WITH SELF-ADAPTIVE REGION

Effective guidance is one of the most important tasks to the performance of air-to-air missile. The fuzzy logic controller is able to perform effectively even in situations where the information about the plant is inaccurate and the operating conditions are uncertain. Based on the proportional navigation, the fuzzy logic and the genetic algorithm are combined to develop an evolutionary fuzzy navigation law with self-adapt region for the air-to-air missile guidance. The line of sight (LOS) rate and the closing speed between the missile and the target are inputs of the fuzzy controller. The output of the fuzzy controller is the commanded acceleration. Then a nonlinear function based on the conventional fuzzy logic control is imported to change the region. This nonlinear function can be changed with the input variables. So the dynamic change of the fuzzy variable region is achieved. The guidance law is optimized by the genetic algorithm. Simulation results of air-to-air missile attack using MATLAB show that the method needs less acceleration and shorter flying time, and its realization is simple.[KH*3/4D]

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

基于Dim env-YOLO算法的昏暗场景车辆多目标检测

低照度的夜间路况复杂,现有夜间车辆识别相关研究较少,且存在识别方法实时性不高、过多占用硬件资源等不足。针对夜间场景车辆识别干扰因素较多、检测效果不佳的问题,提出一种基于YOLOv4的Dim env-YOLO车辆目标检测算法。利用MobileNetV3网络替换原始YOLOv4中的主干网络,以减少模型参数量。在改进的YOLOv4模型上使用图像暗光增强方法,提高车辆目标在昏暗环境中的可识别性。在此基础上,引入注意力机制加强特征信息选择,同时利用深度可分离卷积降低网络计算量。选取北京部分道路的夜间场景图片自制数据集并进行实验验证,结果表明,在存在高斯噪声、模糊扰动、雨雾夜晚等情况下,Dim env-YOLO算法的测试结果较稳定,对于照度低于30 lx的昏暗条件下的车流,其检测mAP值达到90.49%,对于最常见的轿车类别,mAP值达到96%以上,优于Faster-RCNN、YOLOv3、YOLOv4等网络模型在昏暗光照条件下的检测效果。

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.