Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.

Quantitative Analysis on Postponement Strategies of Decoupling Points in Mass Customization

The key to mass customizing effectively is postponing the deco upling point of customer order in the supply network. This paper focused on quantitative analysis on postponement strategies of multiple decoupling points in mass customization to improve operating efficiency and quickly meet customer demands with a minimum amount of inventory.

NUMERICAL SIMULATION OF MOLTEN POOL AND CONTROL STRATEGY OF KISS POINT IN A TWIN-ROLL STRIP CASTING PROCESS

Coupled turbulent flow, temperature fields of the twin-roll casting strip process were simulated by three-dimensional finite element method. Based on the heat balance calculation and using inverse methods between the simulations and real experiments, the relational models among casting speed, location, and coefficient of heat transfer between molten metal and rolls in different regions are given. In the simulation, the calculated surface temperatures are in good agreement with the measured values. An on-line model of kiss point is derived by simulations and the geometry of molten pool, corresponding control strategy is also proposed.

基于knee points的改进多目标人工蜂群算法

传统的人工蜂群算法(Artificial Bee Colony algorithm,ABC)及其在多目标上的扩展(Multi Objective Artificial Bee Colony algorithm,MOABC)存在着在高维、多峰函数情况下收敛速度变慢、后期容易陷入局部最优以及寻优精度丢失等问题。基于knee points提高收敛性和分布性的特点,设计了一种快速识别knee point的算法并将其应用到多目标人工蜂群算法中,提出了一种基于knee points的改进多目标人工蜂群算法(Kn MOABC)。算法在迭代过程中考虑pareto支配关系的同时,优先选择knee point作为下一代个体,极大地增强了算法的收敛速度,同时,在knee point识别算法中加入自适应的策略以保持良好的分布性。实验结果表明,Kn MOABC的性能优于三个最新的多目标人工蜂群对比算法。

Adding-Point Strategy for Reduced-Order Hypersonic Aerothermodynamics Modeling Based on Fuzzy Clustering

Reduced order models（ROMs） based on the snapshots on the CFD high-fidelity simulations have been paid great attention recently due to their capability of capturing the features of the complex geometries and flow configurations. To improve the efficiency and precision of the ROMs, it is indispensable to add extra sampling points to the initial snapshots, since the number of sampling points to achieve an adequately accurate ROM is generally unknown in prior, but a large number of initial sampling points reduces the parsimony of the ROMs. A fuzzy-clustering-based adding-point strategy is proposed and the fuzzy clustering acts an indicator of the region in which the precision of ROMs is relatively low. The proposed method is applied to construct the ROMs for the benchmark mathematical examples and a numerical example of hypersonic aerothermodynamics prediction for a typical control surface. The proposed method can achieve a 34.5% improvement on the efficiency than the estimated mean squared error prediction algorithm and shows same-level prediction accuracy.

A station-keeping strategy for collinear libration point orbits

Spacecrafts in periodic or quasi-periodic orbits near the collinear libration points are proved to be excellent platforms for scientific investigations of various phenomena.Since such periodic or quasi-periodic orbits are exponentially unstable,the station-keeping maneuver is needed. A station-keeping strategy which is found by an analytical method is presented to eradicate the dominant unstable component of the libration point trajectories.The inhibit force transforms the unstable component to a stable component.In this method,it is not necessary to determine a nominal orbit as a reference path.

SGT-Net: A Transformer-Based Stratified Graph Convolutional Network for 3D Point Cloud Semantic Segmentation

In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.

Construction Project Management and Construction Quality Control Strategy

With the gradual acceleration of the urbanization process,the construction industry has been developing rapidly.As the key link to ensure the quality and safety of the project,construction management and construction quality control are of great significance to enhance the competitiveness of construction enterprises and realize sustainable development.In this paper,the effective strategy of construction management and the effective strategy of construction quality control will be discussed in depth,aiming at providing useful management and quality control strategies for construction enterprises.

A simulation study of sampling time point designing for therapeutic drug monitoring based on a population pharmacokinetic model

Aim To develop a method to estimate population pharmacokinetic parameters with the limited sampling time points provided clinically during therapeutic drug monitoring. Methods Various simulations were attempted using a one-compartment open model with the first order absorption to determine PK parameter estimates with different sampling strategies as a validation of the method. The estimated parameters were further verified by comparing to the observed values. Results The samples collected at the single time point close to the non-informative sampling time point designed by this method led to bias and inaccurate parameter estimations. Furthermore, the relationship between the estimated non-informative sampling time points and the values of the parameter was examined. The non-informative sampling time points have been developed under some typical occasions and the results were plotted to show the tendency. As a result, one non-informative time point was demonstrated to be appropriate for clearance and two for both volume of distribution and constant of absorption in the present study. It was found that the estimates of the non-informative sampling time points developed in the method increase with increases of volume of distribution and the decrease of clearance and constant of absorption. Conclusion A rational sampling strategy during therapeutic drug monitoring can be established using the method present in the study.

Load-Aware Offloading Strategy in Two-Tier Heterogeneous Network

Although small cell offloading technology can alleviate the congestion in macrocell, aggressively offloading data traffic from macrocell to small cell can also degrade the performance of small cell due to the heavy load. Because of collision and backoff, the degradation is significant especially in network with contention-based channel access, and finally decreases throughput of the whole network. To find an optimal fraction of traffic to be offloaded in heterogeneous network, we combine Markov chain with the Poisson point process model to analyze contention-based throughput in irregularly deployment networks. Then we derive the close-form solution of the throughput and find that it is a function of the transmit power and density of base stations.Based on this, we propose the load-aware offloading strategies via power control and base station density adjustment. The numerical results verify our analysis and show a great performance gain compared with non-load-aware offloading.

A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking

Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.

Open Loop Saddle Point on Linear Quadratic Stochastic Differential Games

In this paper, we deal with one kind of two-player zero-sum linear quadratic stochastic differential game problem. We give the existence of an open loop saddle point if and only if the lower and upper values exist.

OUTPUT MAXIMIZATION CONTROL FOR VSCF WIND ENERGY CONVERSION SYSTEM USING EXTREMUM CONTROL STRATEGY

The energy conversion optimization control strategy is presented for a family of horizontal-axis variablespeed fixed-pitch wind energy conversion systems,working in the partial load region.The system uses a variablespeed wind turbine(VSWT)driving a squirrel-cage induction generator(SCIG)connected to a grid.A new maximum power point tracking(MPPT)approach is proposed based on the extremum seeking control principles under the assumption that the wind turbine model and its parameters are poorly known.The aim is to drive the average position of the operation point close to optimality.Here the wind turbulence is used as search disturbance instead of inducing new sinusoidal search signals.The discrete Fourier transform(DFT)process of some available measures estimates the distance of operation point to optimality.The effectiveness of the proposed MPPT approach is validated under different operation conditions by numerical simulations in MATLAB/SIMULINK.The simulation results prove that the new approach can effectively suppress the vibration of system and enhance the dynamic performance of system.

A geological and functional “three-step strategy” for renminbi's internationalization

Internationalization of the renminbi is the only option for China to fully integrate into the global economic and financial systems. It is not only necessary but imperative as well. With the discussion of advancing renminbi internationalization, benef its and costs must be addressed. We need to closely monitor possible risks and bring out the benefits while averting disadvantages. The overall strategy is to consolidate the groundwork and at the same time actively advance renminbi internationalization and opt for a "three-step" strategy at both strategic and policy levels. To overcome the constraints standing in the way of renminbi internationalization, the basic goals should be: to enhance the readiness of renminbi internationalization, dialectically assess the preconditions, and increase the currency's international circulation as a way to achieve major progress in renminbi internationalization.

Economic Load Dispatch Based on Efficient Population Utilization Strategy for Particle Swarm Optimization

In this paper, the efficient population utilization strategy for particle swarm optimization (EPUSPSO) is proposed to solve the economic load dispatch (ELD) problem of power system. This algorithm improves the accuracy and the speed of its convergence by changing the number of particles effectively, and improving the velocity equation and position equation. In order to verify the effectiveness of the algorithm, this algorithm is tested in three different ELD cases of power system include IEEE 3-unit case, 13-unit case, and 40-unit case, and the obtained results are compared with those obtained from other algorithms using the same system parameters. The compared results show that the algorithm can find the optimal solution effectively and accurately, and avoid falling into the local optimal problem;meanwhile, faster speed can be ensured in the case.

适用于中点箝位型三电平逆变器的多目标调制策略

为同时实现中点箝位型三电平逆变器的中点电压平衡和共模电压降低,分别分析了基于载波脉宽调制(CBPWM)和基于虚拟空间矢量脉宽调制(VSVPWM)的中点电压平衡和共模电压降低方法。基于CBPWM提出一种可以降低共模电压的载波脉宽调制(RCMV_CBPWM)策略,可将大多数工作区域的共模电压限制在直流侧电压的1/6以内。为了实现上述2个目标,同时不过分增加开关损耗,在RCMV_CBPWM基础上提出了一种多目标调制(MOPWM)策略,在全调制度和全负载功率因数范围内,共模电压可限制在直流侧电压的1/6以内并实现中点电压平衡,且其开关损耗低于VSVPWM。比较了MOPWM、RCMV_CBPWM和VSVPWM在中点电压控制、共模电压降低和开关损耗降低方面的性能。实验结果验证了MOPWM的可行性和优越性。

我国农业面源污染研究与展望

农业面源污染会带来一定的生态环境风险。经过20多年的治理,我国面源污染基础研究和防控工作取得重要进展,构建了流域、区域或国家尺度农业面源污染源汇路径全过程监测与评估体系,基本厘清了不同生态条件下农田系统中氮磷流失的主控因子,初步探明了农田有毒有害化学/生物污染与防控机制。在防控技术层面,研发出新型、绿色肥料替代常规化肥减量投入技术与产品,氮、磷高效利用污染负荷减排技术,土壤氮、磷增容负荷减排技术,坡耕地氮磷流失阻控技术,农业废弃物资源化利用技术与消纳装备。我国农业氮、磷排放污染负荷虽整体呈下降趋势,但依然是影响我国环境质量的首要污染因子,且农业面源污染呈现新的特征,表现为养殖业尤其是水产养殖业污染问题逐渐突出,残留地膜、农药、抗生素、激素、病原微生物等新型污染物污染问题不容忽视,面源污染途径从以径流扩散为主转向淋溶、挥发、累积并重的阶段,污染介质从单一地表水体转向包括地下水体、大气以及农田生态系统并存的状态。未来应以本领域我国取得科研成果和相关信息资源为基础,利用数字技术和AI技术,构建我国农业面源污染防控技术和管理融合大数据模型和系统理论,持续加强田块、流域、区域等多尺度面源污染防控技术集成配套与工程化应用研究,培育绿色低碳农业新质生产力产业,落实防控技术工程“政—产—学—研—用”保障机制与措施,以提升区域或流域尺度面源污染防控技术的环境、经济和社会综合效益,全面推进我国农业面源污染治理工作,促进农业高质量健康发展。

障碍物无规则运动下启发式Gauss配点参数化连续避障规划

针对障碍物无规则运动下自动驾驶车辆(SDV)的避障轨迹规划,本文提出一种结合模型约束简化和启发式非均匀Gauss配点参数化的轨迹规划算法.首先,结合SDV运动学方程和约束条件建立了SDV连续避障轨迹规划的问题;然后,在高斯伪谱法离散化算法框架下,采用Gauss配点对状态变量进行离散化;进一步,提出了启发式Gauss配点初始化策略,以此提升SDV求解速度进而实现避障实时规划;同时,对经过约束处理后的轨迹进行验证,以此保证规划轨迹的安全性;最后,在车辆模型上针对不同障碍物运动场景进行仿真测试,验证提出方法的有效性和实时性.结果显示,本文方法可以有效进行避障轨迹规划,并确保轨迹的安全性,轨迹规划平均求解耗时维持在25ms左右,显示出本文方法在实时避障规划的效能和实际应用价值.

大风扰动对深空探测大天线指向的影响分析

深空测控通信通常采用提高载波频率、增大抛物面天线口径等措施来增强设备的跟踪性能,但同时带来了天线的半功率波束宽度变窄、动态特性降低和天线抗阵风扰动能力变弱等问题。大风扰动引起的天线变形和指向偏差会导致深空探测的增益损失,增益下降会引起测控跟踪不稳定、数据误码率增加、接收机失锁和遥控无法完成等情况,影响测控任务的正常执行。针对大风扰动对我国某深空站转台式35 m A-E座架双反射面天线执行深空探测任务的影响,通过实测数据统计分析了场区全年风速、风向的变化特点,对跟踪弧段内风速与天线指向角度误差数据进行了对比分析,仿真计算了在不同风速、风向情况下天线指向角度误差及对上下行测控链路增益的影响,结合设备现有技术设施,提出了在执行实时任务中的应对策略,降低了大风扰动对测控任务的影响,提高了设备任务执行能力。

基于筛选策略的动态环境下激光SLAM算法

现有的同步定位与建图(SLAM)方法在理想条件下运行稳定,但在动态环境中会因移动物体特征点云的误匹配导致定位误差增大。为解决此问题,提出了一种动态点云检测算法。首先利用惯性测量装置信息对点云数据预处理,包含去畸变等操作;然后剔除地面点云,采用弯曲体素结构对非地面点云进行聚类;接着,通过匈牙利算法关联和匹配两帧之间的聚类,同时利用惯性信息统一坐标系;最后设计一种筛选策略,先用边界框交并比和质心速度粗略筛选动态聚类,再用z轴(高度)分布相似性进行精细筛选。实验结果表明,所提算法能够识别并滤除实验环境中的大部分动态点云聚类;与LIO-SAM算法相比,四种场景下的定位均方根误差平均降低了17.75%;平均精确率和召回率相比Removert分别提升14.81%和5.90%。