Adaptive energy-preserving algorithms for guiding center system

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized plasmas.The adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving algorithms.These new adaptive algorithms are second order,and their algebraic order is carefully studied.Numerical results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

A Novel Radius Adaptive Based on Center-Optimized Hybrid Detector Generation Algorithm

Negative selection algorithm(NSA)is one of the classic artificial immune algorithm widely used in anomaly detection.However,there are still unsolved shortcomings of NSA that limit its further applications.For example,the nonselfdetector generation efficiency is low;a large number of nonselfdetector is needed for precise detection;low detection rate with various application data sets.Aiming at those problems,a novel radius adaptive based on center-optimized hybrid detector generation algorithm(RACO-HDG)is put forward.To our best knowledge,radius adaptive based on center optimization is first time analyzed and proposed as an efficient mechanism to improve both detector generation and detection rate without significant computation complexity.RACO-HDG works efficiently in three phases.At first,a small number of self-detectors are generated,different from typical NSAs with a large number of self-sample are generated.Nonself-detectors will be generated from those initial small number of self-detectors to make hybrid detection of self-detectors and nonself-detectors possible.Secondly,without any prior knowledge of the data sets or manual setting,the nonself-detector radius threshold is self-adaptive by optimizing the nonself-detector center and the generation mechanism.In this way,the number of abnormal detectors is decreased sharply,while the coverage area of the nonself-detector is increased otherwise,leading to higher detection performances of RACOHDG.Finally,hybrid detection algorithm is proposed with both self-detectors and nonself-detectors work together to increase detection rate as expected.Abundant simulations and application results show that the proposed RACO-HDG has higher detection rate,lower false alarm rate and higher detection efficiency compared with other excellent algorithms.

The Precise Methods for the Measurement of Collimator Hole Angulation and Center of Rotation of SPECT by Adaptive Quality Control Phantom

The Adaptive Quality Control Phantom (AQCP) is a computer-controlled phantom which positions and moves a radioactive source in the Field of View (FOV) of an imaging nuclear medicine device on a definite path to produce a spatial distribution of gamma rays to perform QC Tests such as the Collimator Hole Angulation (CHA) and the Center of Rotation (COR) of Single Photon Emission Computer Tomography (SPECT). The collimator hole angulation for six collimators was measured using a point source and a computer-controlled cylindrical positioning system. In this method, the displacement of the image of a point source was examined as the AQCP was moving point source vertically away from the collimator face. The results of the high-accuracy measurement method of CHA show that the measurement accuracy for absolute angulation errors is better than ±0.024°. The Root Mean Square (RMS) of CHA for LEHR, LEHS and LEUHR collimators of SMV dual heads camera and LEGP, MEGP and HEGP of GE Millennium MG were evaluated to be 0.290°, 0.292°, 0.208°, 0.154°, 0.220° and 0.202°, respectively. It is to be added in this connection that the evaluated RMS of CHA for LEHR collimator with the distance variation from the collimator’s surface ±1 mm has been varied ±0.04 degree. A new method for the center of rotation assessment by AQCP is introduced and the results of this proposed method as compared with the routine QC test and their differences are discussed in detail. We defined and measured a new parameter called Dynamic Mechanical Error (DME) for applying the gantry motion correction.

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.

An Active Anti-Jamming Approach for Frequency Diverse Array Radar with Adaptive Weights

Due to the rapid development of electronic countermeasures(ECMs),the corresponding means of electronic counter countermeasures(ECCMs)are urgently needed.In this paper,an act-ive anti-jamming method based on frequency diverse array radar is proposed.By deriving the closed form of the phase center in a uniform line array FDA,we establish a model of the FDA signal based on adaptive weights and derive the effect of active anti-jamming in this regime.The pro-posed active anti-jamming method makes it difficult for jammers to detect or locate our radar.Fur-thermore,the effectiveness of the two frequency increment schemes in terms of anti-jamming is ana-lyzed by comparing the deviation of phase center.Finally,the simulation results verify the effective-ness and superiority of the proposed method.

ADAPTIVE DELAUNAY TRIANGULATION WITH MULTIDIMENSIONAL DISSIPATION SCHEME FOR HIGH-SPEED COMPRESSIBLE FLOW ANALYSIS

Adaptive Delaunay triangulation is combined with the cell-centered upwinding algorithm to analyze inviscid high-speed compressible flow problems. The multidimensional dissipation scheme was developed and included in the upwinding algorithm for unstructured triangular meshes to improve the computed shock wave resolution. The solution accuracy is further improved by coupling an error estimation procedure to a remeshing algorithm that generates small elements in regions with large change of solution gradients, and at the same time, larger elements in other regions. The proposed scheme is further extended to achieve higher-order spatial and temporal solution accuracy. Efficiency of the combined procedure is evaluated by analyzing supersonic shocks and shock propagation behaviors for both the steady and unsteady high-speed compressible flows.

Hybrid Multi-Object Optimization Method for Tapping Center Machines

This paper proposes a hybrid multi-object optimization method integrating a uniform design,an adaptive network-based fuzzy inference system(ANFIS),and a multi-objective particle swarm optimizer(MOPSO)to optimize the rigid tapping parameters and minimize the synchronization errors and cycle times of computer numerical control(CNC)machines.First,rigid tapping parameters and uniform(including 41-level and 19-level)layouts were adopted to collect representative data for modeling.Next,ANFIS was used to build the model for the collected 41-level and 19-level uniform layout experiment data.In tapping center machines,the synchronization errors and cycle times are important consid-erations,so these two objects were used to build the ANFIS models.Then,a MOPSO algorithm was used to search for the optimal parameter combinations for the two ANFIS models simultaneously.The experimental results showed that the proposed method obtains suitable parameter values and optimal parameter combinations compared with the nonsystematic method.Additionally,the optimal parameter combination was used to optimize existing CNC tools during the commissioning process.Adjusting the proportional and integral gains of the spindle could improve resistance to deformation during rigid tapping.The posi-tion gain and prefeedback coefficient can reduce the synchronization errors significantly,and the acceleration and deceleration times of the spindle affect both the machining time and synchronization errors.The proposed method can quickly and accurately minimize synchronization errors from 107 to 19.5 pulses as well as the processing time from 3,600 to 3,248 ms;it can also shorten the machining time significantly and reduce simultaneous errors to improve tapping yield,there-by helping factories achieve carbon reduction.

“里和”与“外应”:气候变化下环湖地区的适应性治理

气候变化给青藏高原带来显著影响,更是对当地农牧民的生计造成冲击,研究当地气候变化影响及牧民应对策略,不仅是提高气候变化适应能力的起点,也是建立适应气候变化区域格局的重要支撑。基于2020和2023年对案例地Z村的两次实地调研,通过牧户问卷调查和深度访谈,按照适应性治理的三层特征,分析当地气候变化适应的成效与挑战。研究发现:面对降水和气温的变化、湖面扩大、植被变化和灾害影响,牧民积极采取多种办法来减少气候变化的影响,呈现较高的适应性与恢复力。这些适应行动的成功经验源于“里和”,包括人与人之间的合作以及人与自然间的和谐律动。但是“外应”不足仍是挑战,即案例地缺少必要的外部支持,不仅包括直接灾害救助,还包括对其适应能力可持续性的保护和应对潜在的草原生态变化。如果“外应”持续低迷,现有的适应能力终会耗尽。提出建立适应气候变化区域格局应遵循多中心治理的思路,将多个利益相关者联合起来,汇总不同地区气候变化应对的成功经验,形成多种知识的融合,才能形成对地方的有效“外应”,提高中国气候变化适应能力。

家庭为中心的引导式宣教联合适应性训练对高胆红素血症患儿家属心理状态和患儿恢复的影响

目的:探讨家庭为中心的引导式宣教联合适应性训练对高胆红素血症(NHB)患儿家属心理状态和患儿恢复的影响。方法:选取2023年1月-2024年1月在某院收治的86例NHB患儿及86名主要照顾者为研究对象,随机数表法分为观察组(予以家庭为中心的引导式宣教联合适应性训练)与对照组(常规护理),每组43例NHB患儿及其43名主要照顾者。均护理干预至患儿出院,对比两组患儿恢复情况(胎便排出时间、住院时间、黄疸消退时间、日均睡眠时间)、神经功能状况[胆红素致神经功能障碍量表(BIND)]、发育情况[Gesell发育诊断量表(GDDS)]、患儿家属心理状态[抑郁自评量表(SDS)、焦虑自评量表(SAS)]、家属满意度[纽卡斯尔护理服务满意度量表(NSNS)]。结果:观察组黄疸消退时间、胎便排出时间、住院时间均短于对照组,日均睡眠时间长于对照组(t=5.411,7.247,5.262,9.331;P<0.01)。干预后两组GDDS(适应行为、大运动行为、精细运动行为、语言行为、个人-社交行为)评分均升高,BIND评分均降低,且观察组GDDS各项评分高于对照组,BIND评分低于对照组(t=4.008,2.353,3.129,5.544,2.371,12.709;P<0.05)。干预后两组患儿家属SDS、SAS评分均降低,且与对照组相比,观察组上述评分均更低(P<0.05)。观察组患儿家属满意度相较于对照组更高(χ^(2)=3.957,P<0.05)。结论:家庭为中心的引导式宣教联合适应性训练对NHB患儿健康恢复和发育情况均有促进作用,并有利于改善患儿神经功能障碍和患儿家属心理状态,提高患儿家属满意度。

采用自适应中心差分卡尔曼滤波器的锂离子电池荷电状态估计

锂离子电池因其能量密度高、使用寿命长等优点被越来越多地应用到卫星、便携式设备和电动汽车等领域.荷电状态作为电池管理系统的重要指标,它的准确监测对保障电池的使用安全、提高电池的使用效率有着重要意义.针对锂离子电池的荷电状态估计,本文提出了一种自适应中心差分卡尔曼滤波算法.首先,设计了一个线性卡尔曼滤波器实现了对测量方程系数的实时估计,从而避免了荷电状态与开路电压关系曲线的测试.其次,考虑到部分工况难以准确地获取模型参数,使用增广向量法并采用自适应中心差分卡尔曼滤波器实现了荷电状态与模型参数的自适应估计.再次,将线性卡尔曼滤波器与自适应中心差分卡尔曼滤波器耦合,实现了荷电状态、模型参数、测量方程系数的联合估计,使得本文所提算法能够更好地应用于电池内部参数未知的复杂工况.为了进一步提高算法的估计精度和对噪声的适应能力,通过迭代法对噪声协方差矩阵进行了动态调整.最后,通过几组实验验证了本文所提算法的有效性.

基于交通拥堵信息的高速公路拥堵路段ACK-Means聚类

为了充分利用实际高速公路路段交通拥堵信息,更合理地聚类交通拥堵的内在规律和特征变化,提出自适应确定聚类中心C和类别K值(adaptive center and K-means value,ACK-Means)的聚类算法,进行高速公路拥堵路段聚类。ACK-Means算法借助簇类密度、簇类间距以及簇类强度,同时又考虑到数据样本的偶然性,对离群点进行合理分配,ACK-Means算法可实现自适应确定聚类中心C和类别K值。基于实际交通拥堵信息构建数据集,Python编程实现高速公路拥堵路段ACK-Means聚类,巧妙解决了高速公路拥堵路段聚类数目K和聚类中心C设定问题。聚类结果表明,ACK-Means算法实现高速公路拥堵路段无监督聚类,聚类结果完全基于实际的高速公路交通拥堵信息,具有更高的实用性。

面向复杂载荷工况下的横梁多分辨率拓扑优化

针对复杂载荷条件下加工中心横梁结构的刚度优化问题,提出了一种可以有效降低模型计算规模的高精度拓扑优化方法。首先,对加工中心横梁这类结构复杂、求解规模大的拓扑优化问题进行分析,发现多分辨率拓扑优化方法可以显著降低此类问题的计算成本;其次,为进一步提高计算效率,将自适应梯度分区技术引入到多分辨率拓扑优化方法中,形成一种自适应梯度分区的多分辨率拓扑优化方法,该方法在模型拓扑更新过程中只对特定的低效离散区域进行处理,以达到进一步降低模型计算规模的目的;最后,在加工中心横梁多工况优化中,将自适应梯度分区的多分辨率拓扑优化与传统的优化方法进行对比研究,发现在相同单元网格数量条件下,新的拓扑优化方法的计算速度相较于传统的拓扑优化方法提高了约8倍。

基于P-中心模型的城市轨道交通线网应急救援站选址研究

[目的]随着城市轨道交通线网的扩大,其系统风险变得更为复杂且不可控。为提高城市轨道交通系统的应急救援能力,需对线网内应急救援站的选址问题展开研究。[方法]将地铁覆盖区域按照城市道路环线进行分区限速,得到任意路径的速度矩阵,结合距离矩阵进行转换后得到时间矩阵。在此基础上,计算任意站点间基于时间的最短路径。考虑时间、救援关系、成本3个方面的约束条件,建立了以系统最大救援时间最小化为目标的P-中心选址模型。以武汉市城市轨道交通线网应急救援站选址为案例,应用该模型,并采用自适应遗传算法在MATLAB软件中进行编程求解,得出该线网应急救援站选址的最优方案。[结果及结论]与相似研究相比,该选址方案投入更少但效益更大,充分证明了所提的P-中心模型具有先进性。

旋转测量状态下光斑中心定位方法

目前,对管道内腔为自由曲面的截面轮廓进行高精度测量是行业难题。拟将多个不同测量范围的激光位移传感器放置于管道内腔中,采用旋转的方式对内腔进行测量,将得到的测量数据进行归一化处理后获得被测管道内腔截面轮廓形状。分析测量过程中激光光斑的变化情况,针对光斑图像的多峰、散斑、平顶与形状变化等现象,提出了一种基于传统灰度重心法进行粗定位的距离倒数加权多项式插值亚像素光斑中心定位方法。采用中值滤波对光斑图像进行预处理,通过自适应阈值分割法从环境背景光强中分离出光斑中心定位区域。通过MATLAB仿真与灰度重心法、加权灰度重心法与高斯拟合法传统光斑中心定位方法相比,结果表明,光斑中心定位精度达到0.01 pixel,在激光光斑中心定位精度和稳定性方面有一定的改善,明显优于传统的光斑中心定位算法。

综合性社区中心适老性使用后评价——广深案例实证

综合性社区中心可提供较为便捷的“一站式”日常生活服务,日渐成为支持社区养老生活的重要共享平台。为研究这类设施的使用实效和适老性能进行实证研究,选取广深地区有代表性的五个综合性社区中心为研究对象,聚焦建筑的适老性目标,采用量化和质化有机结合的使用后评价研究方法,调查样本社区中心使用者的行为、心理和满意度,评估现状空间的使用状况与适老性特征。在此基础上就现存问题提出平衡社区中心公共服务与商业功能的比例、强化空间整体性和连续性、注重适老化精细设计、老幼空间复合、延长公共空间服务时间并分时管理复合使用等优化建议。

基于中心偏差估计和自适应间隔的人脸识别算法

损失函数的设计在深度人脸识别中至关重要.常见做法是给所有类别添加固定的间隔项,以修改类别间的决策边界,压缩类内特征间距,提高模型分离不同类别特征的能力.然而,为所有类别添加相同的间隔项可能会忽略人脸识别数据集内类别间的不一致性.为进一步提升模型效果,模型应依据类别的学习难易程度,对不同类别样本特征给予不同程度的关注.文中设计了基于类均值中心与类权重中心之间的偏差挖掘难类的方法,称之为中心偏差估计.本文提出的方法会根据中心偏差估计的程度,为不同类别自适应分配不同大小的间隔项.同时,为解决训练前期中心偏差计算不稳定问题,提出了动态变化的收敛参数,调整中心偏差估计的可信度,开展相关实验验证收敛参数的有效性.在人脸验证基准数据集中,本文提出的方法比基准方法的平均准确率提高了0.26%,达到96.62%.在2个大型人脸验证测试数据集上,在FPR等于0.01%时,提出方法的TPR分数分别提高了0.58%和0.22%,获得88.47%和92.29%的实验结果,且多组实验结果表明提出的方法优于一般现有算法.实现代码参见https://github.com/TCCof-WANG/FR-Centers-Bias.

自适应引力密度峰值聚类优化算法

针对密度峰值聚类(Density Peak Clustering,DPC)算法对截断距离的取值较为敏感,密度度量标准不统一且人为选取聚类中心存在主观性的问题,提出了一种自适应引力密度峰值聚类优化(Optimized Adaptive Gravitational Density Peak Clustering Algorithm,OAGDPC)算法。首先采用模糊加权K-近邻技术(Fuzzy Weighted KNearest Neighbors Density Peak Clustering,FKNN-DPC)重新定义了局部密度,统一了密度度量的标准;然后提出一种自适应选择聚类中心的策略,结合基于引力的密度峰值(Gravitational Density Peak Clustering,GDPC)算法中牛顿万有引力定律与DPC算法的参数映射,使用引力类比距离,并设置综合考虑局部密度和引力的决策参数,依据决策参数降序折线图的顶角变化自适应确定聚类中心;最后聚集非中心点并识别异常点。实验选取DPC、GDPC、FKNN-DPC和OAGDPC在人工和UCI数据集上进行测试,结果表明,OAGDPC算法在各数据集上都有良好的表现,特别在聚类结果准确性、自适应能力、鲁棒性方面相对于对比算法具有明显优势。

基于改进布谷鸟算法的物流配送中心选址研究

近年来,物流行业不断发展壮大,配送中心作为物流系统中承上启下的关键节点,在减少物流配送成本、提高服务响应效率上具有十分重要的战略价值。针对供应链环节中因多源多点造成的配送成本增加、库存周转率低等痛点,文中提出了一种改进的布谷鸟算法用于解决物流配送中心选址问题。该算法在传统的布谷鸟算法的基础上,引入粒子群优化算法,将两者有效地组合起来进行求解,并在求解过程中增加自适应参数,以避免出现局部最优现象,增加全局搜索能力。实验表明,相较于其他启发式算法,在解决配送中心选址问题上,改进布谷鸟算法能够有效降低物流配送成本,加快求解速度,提高库存周转次数,为企业物流的预算方案提供参考依据。

基于自适应模糊算法的四足机器人全身运动控制

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