Algorithm Selection Method Based on Coupling Strength for Partitioned Analysis of Structure-Piezoelectric-Circuit Coupling

In this study, we propose an algorithm selection method based on coupling strength for the partitioned analysis ofstructure-piezoelectric-circuit coupling, which includes two types of coupling or inverse and direct piezoelectriccoupling and direct piezoelectric and circuit coupling. In the proposed method, implicit and explicit formulationsare used for strong and weak coupling, respectively. Three feasible partitioned algorithms are generated, namely(1) a strongly coupled algorithm that uses a fully implicit formulation for both types of coupling, (2) a weaklycoupled algorithm that uses a fully explicit formulation for both types of coupling, and (3) a partially stronglycoupled and partially weakly coupled algorithm that uses an implicit formulation and an explicit formulation forthe two types of coupling, respectively.Numerical examples using a piezoelectric energy harvester,which is a typicalstructure-piezoelectric-circuit coupling problem, demonstrate that the proposed method selects the most costeffectivealgorithm.

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Calculation of Mass Concrete Temperature Containing Cooling Water Pipe Based on Substructure and Iteration Algorithm

Mathematical physics equations are often utilized to describe physical phenomena in various fields of science and engineering.One such equation is the Fourier equation,which is a commonly used and effective method for evaluating the effectiveness of temperature control measures for mass concrete.One important measure for temperature control in mass concrete is the use of cooling water pipes.However,the mismatch of grids between large-scale concrete models and small-scale cooling pipe models can result in a significant waste of calculation time when using the finite element method.Moreover,the temperature of the water in the cooling pipe needs to be iteratively calculated during the thermal transfer process.The substructure method can effectively solve this problem,and it has been validated by scholars.The Abaqus/Python secondary development technology provides engineers with enough flexibility to combine the substructure method with an iteration algorithm,which enables the creation of a parametric modeling calculation for cooling water pipes.This paper proposes such a method,which involves iterating the water pipe boundary and establishing the water pipe unit substructure to numerically simulate the concrete temperature field that contains a cooling water pipe.To verify the feasibility and accuracy of the proposed method,two classic numerical examples were analyzed.The results showed that this method has good applicability in cooling pipe calculations.When the value of the iteration parameterαis 0.4,the boundary temperature of the cooling water pipes can meet the accuracy requirements after 4∼5 iterations,effectively improving the computational efficiency.Overall,this approach provides a useful tool for engineers to analyze the temperature control measures accurately and efficiently for mass concrete,such as cooling water pipes,using Abaqus/Python secondary development.

Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier

The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm

Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value.

Review on Service Curves of Typical Scheduling Algorithms

In recent years,various internet architectures,such as Integrated Services(IntServ),Differentiated Services(DiffServ),Time Sensitive Networking(TSN)and Deterministic Networking(DetNet),have been proposed to meet the quality-of-service(QoS)requirements of different network services.Concurrently,network calculus has found widespread application in network modeling and QoS analysis.Network calculus abstracts the details of how nodes or networks process data packets using the concept of service curves.This paper summarizes the service curves for typical scheduling algorithms,including Strict Priority(SP),Round Robin(RR),Cycling Queuing and Forwarding(CQF),Time Aware Shaper(TAS),Credit Based Shaper(CBS),and Asynchronous Traffic Shaper(ATS).It introduces the theory of network calculus and then provides an overview of various scheduling algorithms and their associated service curves.The delay bound analysis for different scheduling algorithms in specific scenarios is also conducted for more insights.

Prediction Model of Wax Deposition Rate in Waxy Crude Oil Pipelines by Elman Neural Network Based on Improved Reptile Search Algorithm

A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.

基于PI建模和反步滑模控制的主动波浪补偿策略

海上起重船受风、浪、涌影响会产生剧烈的船舶姿态变化,造成起重机和货物的位姿变化,对货物和人员存在安全隐患,波浪补偿平台的稳定性控制能有效减少复杂海况下船舶运动对海上作业安全性、稳定性和精准性的影响,对浮式起重船海上设备精准装载作业极其重要。针对补偿平台的迟滞非线性导致的建模困难和控制不精确问题,本文提出基于PI(Prandtle–Ishlinskii)建模和反步滑模控制的主动波浪补偿策略。首先,通过实验得到补偿系统的迟滞效应曲线,分析系统迟滞环建立PI迟滞模型,并采用递推最小二乘法辨识模型的各个参数,从而求得系统模型。然后,基于李雅普诺夫(Lyapunov)稳定性设计反步控制补偿方法,并结合滑模控制规律加快初始控制速度。最后,将反步滑模法应用于补偿系统,采用MATLAB软件仿真在规则波和不规则波下的响应来验证算法和模型的正确性,并在工控机中用C#编写控制程序,驱动运动控制卡控制伺服电机带动电缸进行补偿运动,同时通过传感器采集系统运动的实时数据,并反馈给工控机形成闭环,以期验证补偿平台在补偿规则波和不规则波下的补偿效果。实验结果表明,所建立的斯图尔特(Stewart)浮式平台中,PI迟滞模型具有良好的精度,反步终端滑模控制算法在Stewart平台的实际控制中能够很好地补偿波浪运动,相比比例–积分–微分控制(PID)、反步法、强化学习等控制方法,反步终端滑模方法能快速较好跟踪期望位移,补偿精度达到0.9729。

益智仁-乌药药对调控PI3K/Akt/mTOR通路介导细胞自噬保护肾小球足细胞的作用机制研究

目的研究益智仁-乌药药对通过调控PI3K/Akt/mTOR信号通路促进足细胞自噬治疗糖尿病肾病(Diabetic Nephropathy,DN)的作用。方法60只造模成功的C57BL/KSJ-db/db(以下简称db/db)小鼠随机分为模型组、二甲双胍组、缬沙坦组、益智仁-乌药药对(低、中、高剂量)组,每组10只;另取10只C57BL/KSJ-db/m(以下简称db/m)小鼠为正常组,正常组和模型组给予生理盐水,治疗组小鼠分别给予相应药物,给药8周后检测小鼠肾脏病理学改变,足细胞自噬体数量、结构及相关蛋白表达。结果与模型组相比,益智仁-乌药药对组可显著减轻糖尿病肾病小鼠肾小球基底膜增厚情况,增加足细胞自噬体数量,显著升高自噬相关蛋白表达(P<0.05),降低PI3K/Akt/mTOR信号通路相关蛋白的表达(P<0.05)。其中益智仁-乌药药对高剂量组各指标改善优于益智仁-乌药低、中剂量组。结论益智仁-乌药药对通过抑制PI3K/Akt/mTOR信号通路激活,提高足细胞自噬水平,减轻足细胞损伤,发挥治疗糖尿病肾病的作用。

基于Raspberry Pi的安全帽识别系统设计与实现

为了便于施工危险区域人员的自动化识别,提出了一种基于Raspberry Pi的安全帽识别算法。该算法将摄像头采集到的原始视频图像进行滤波、形态学等处理,再对图像中的安全帽进行识别。对于彩色安全帽,将原始图像转换到HSV空间,根据不同颜色色调阈值的设定同时识别红、黄、蓝三种颜色的安全帽,并结合形状特征剔除错误目标。对于白色安全帽,将原始图像转化成B通道下的灰度图像,解决了将黄色误检为白色的问题。采用V通道直方图均衡化的方法,提升了昏暗光线条件下的图像亮度。实验结果表明:在无需提前训练的情况下,算法对于单色安全帽识别准确率超过了91%,对于多色安全帽识别率超过了90%,为施工危险区域的安全隐患排查和作业管控提供了解决方案。

基于非线性动态重心粒子群优化的分数阶PI^(λ)D^(μ)控制器设计

针对现有Oustaloup滤波器拟合精度不佳、结构复杂的缺点,提出了最优精简Oustaloup滤波器。针对粒子群优化算法整定分数阶PI^(λ)D^(μ)控制器参数时学习能力不充分、迭代收敛乏力的问题,提出了一种改进的粒子群优化算法。该算法设计了双异步非线性动态学习因子,以提高粒子的思考能力与信息共享能力,并增加了粒子群质量重心项,用以加速收敛过程。将改进的算法结合最优精简Oustaloup滤波器应用于分数阶PI^(λ)D^(μ)控制器的设计过程,选取了2个分数阶系统模型进行仿真验证。结果表明,改进的算法收敛速度更快且不易陷入局部最优,所设计的控制系统超调量更小、调节时间更短、稳态误差更小,提高了系统的抗干扰能力。

自拟平衡针灸通过调控PI3K-AKT信号通路及血清GABA水平对老年失眠的治疗作用

目的探讨自拟平衡针灸通过调控磷脂酰肌醇3激酶(PI3K)-蛋白激酶B(AKT)信号通路及血清氨基丁酸(GABA)水平对老年失眠的治疗作用。方法以老年失眠患者120例作为研究对象,按照随机分组原则分为研究组及对照组,各60例。两组均采取阿普唑仑进行治疗,研究组在此基础上联合采取自拟平衡针灸进行治疗,两组均治疗4 w。比较两组治疗效果、临床改善指标、PI3K-AKT信号通路及GABA、多导睡眠监测仪指标、睡眠质量之间的差异。结果研究组治疗总有效率显著高于对照组(P<0.05)。治疗后,两组睡眠潜伏期、睡眠总时间及觉醒次数均显著改善,且研究组睡眠潜伏期、觉醒次数显著低于对照组(P<0.05),睡眠总时间显著高于对照组(P<0.05)。两组PI3K、AKT及GABA均显著改善,且研究组PI3K、AKT显著低于对照组,GABA显著高于对照组(P<0.05)。两组总睡眠时间(TST)、睡眠效率(SE),第一(TS1)、二(TS2)、三(TS3)及四期(TS4)睡眠、快速眼动睡眠时间(REM)、觉醒期时间(WASO)、睡眠潜伏期时间(SL)均显著改善,且研究组以上指标改善均显著优于对照组(P<0.05)。两组日间功能障碍、睡眠质量、睡眠时间、睡眠障碍及入睡时间均显著改善,且研究组日间功能障碍、睡眠质量、睡眠时间、睡眠障碍及入睡时间显著优于对照组(P<0.05)。结论自拟平衡针灸通过调控PI3K-AKT信号通路及血清GABA水平,有效降低局部炎性反应,优化神经系统的递质传递,有效改善患者的治疗效果。

达沙替尼基于PI3K/AKT信号通路调节乳腺癌细胞生物学行为

目的:基于PI3K/AKT信号通路探讨达沙替尼(dasatinib,DAS)对乳腺癌MCF-7细胞生物学行为的影响。方法:分别使用噻唑蓝(methyl thiazolyl tetrazolium,MTT)法、Transwell法、流式细胞术和Western blotting法检测DAS不同浓度(0、2、6、10μmol/L)作用下MCF-7细胞增殖、侵袭和迁移、细胞凋亡以及PI3K/AKT信号通路相关蛋白表达情况。同时设置对照组(溶媒对照)、DAS组(DAS 10μmol/L)、PI3K抑制剂组(LY29400220μmol/L)、联合组(DAS 10μmol/L+LY29400220μmol/L),比较各组细胞增殖、侵袭和迁移、细胞凋亡以及PI3K/AKT信号通路相关蛋白表达情况。结果:随着DAS作用浓度的升高,MCF-7细胞增殖抑制率和细胞凋亡率升高(P<0.05),侵袭细胞数、迁移细胞数和PI3K、p-PI3K、p-AKT蛋白表达降低(P<0.05)。与对照组相比,DAS组、PI3K抑制剂组、联合组MCF-7细胞增殖抑制率和细胞凋亡率升高(P<0.05),PI3K、p-PI3K、p-AKT蛋白表达降低。与PI3K抑制剂组、DAS组相比,联合组MCF-7细胞增殖抑制率和细胞凋亡率升高,PI3K、p-PI3K、p-AKT蛋白表达降低(P<0.05)。结论:DAS能抑制乳腺癌MCF-7细胞增殖、侵袭和迁移能力,诱导细胞凋亡,其机制可能与调控PI3K/AKT信号通路有关。

PI3K信号过度活化引发免疫病理机制研究进展

目前少数研究发现磷脂酰肌醇3-激酶(PI3K)基因PIK3CD(编码p110δ)发生功能获得性(GOF)变异后,可导致活化的PI3Kδ综合征(APDS)。该突变可通过诱导T细胞减少/衰老/耗竭、B细胞发育受阻、NK细胞毒性降低等多种免疫系统内部缺陷机制导致机体内免疫缺陷、免疫失调甚至肿瘤发生。本文主要对PI3Kδ GOF诱导APDS发生的相关临床疾病特点及免疫缺陷分子机制展开综述,重点阐述该突变导致淋巴细胞发育、分化及功能缺陷的分子机制。

基于改进灰色预测单神经元PI的全超导托卡马克核聚变发电装置快控电源电流控制

全超导托卡马克核聚变发电装置(EAST)快控电源负载电感的电流受多种不确定环境因素的影响而难以预测,灰色预测无需精确对象模型,只需少量已知信息即可实现输出电流短期预测,已在EAST快控电源中有了一定研究应用。为解决灰色预测在EAST快控电源中对突变信号边沿预测精度低和预测延时时间长的问题,提出一种改进灰色预测算法实现输出电流预测。在一个开关周期内对输出电流进行等时长间隔采样4次作为原始序列,将滚动式采样预测改为逐周期采样预测,在实现灰色预测的过程中不必依赖过去几个周期的历史采样信息,只需本周期的4个原始采样值即可实现输出电流的预测。根据预测电流与参考电流误差自适应调整单神经元比例-积分(PI)控制的输出增益,实现输出电流的快速准确控制。仿真和实验结果表明,在EAST快控电源电流预测过程中所提出的改进灰色预测,对比传统灰色预测具有更高的预测精度和更小的预测延时,改进灰色预测变增益单神经元PI控制能够在减小电流超调的同时加快输出电流响应速度。

Peterson图和图D_(m,n)的边PI指数

利用分析法和分类讨论法,给出Peterson图和D_(m,n)图的边PI指数计算公式,丰富了图的PI指数理论.

模糊整定PI参数的双柔性机械臂振动抑制

机械臂伺服驱动系统中柔性因素的存在易导致机械臂输出转速波动,甚至引发系统谐振.为抑制系统转速波动,使机械臂传动系统性能稳定,使用模糊理论整定PI控制器参数的控制策略.根据假设模态法和拉格朗日动力学方程建立了考虑LuGre摩擦模型的双柔性机械臂传动系统动力学方程,并分析了动力学方程中耦合非线性项对系统传动特性的影响.使用极点配置策略确定PI控制器参数的取值范围,后根据模糊规则实时调整控制器参数,以减小伺服系统输出转速的波动,进而抑制系统谐振.最后,借助数值仿真分析和机械臂控制实验,与传统PI控制策略对比,发现本文所述控制策略可使电机端转角跟踪误差绝对值的平均值降低43.066%,柔性负载转角误差的标准差降低46.506%,更加验证了所提模糊规则整定PI控制器参数抑振方法的有效性.

稻瘟病抗性基因Pi2、Pita的特异KASP标记开发与应用

【目的】水稻稻瘟病抗性基因Pi2和Pita是对华南稻区稻瘟病生理小种具有广谱抗性的基因,对水稻的稻瘟病抗性育种具有重要应用价值,开发一套高效的鉴定方法有利于提高水稻抗病品种培育效率。【方法】根据高抗稻瘟病品种‘黄广油占’与高感稻瘟病品种‘广陆矮4号’在Pi2基因的第787位、第788位密码子上的变异GCA GGA/GTG TTA,以及高抗稻瘟病国际稻种质资源Tetep与高感稻瘟病地方种质资源丽江新团黑谷在Pita基因第6 640位碱基上的变异G/T,基于竞争性等位基因PCR(Kompetitive Allele Specific PCR,KASP)标记技术原理,开发抗稻瘟病基因的分子标记。【结果】开发了两个抗稻瘟病基因Pi2和Pita的功能位点KASP标记(W-Pi2、W-Pita),利用标记对广东省农业科学院水稻研究所培育的常规稻、香稻、杂交稻新品种进行检测,筛选出抗性品种19个,其中单个基因检测为抗病等位基因型的水稻品种有13个,两个基因均为抗病等位基因型的品种有6个,比较两个标记结果,Pi2抗性等位基因在育成品种中的频率高于Pita抗性等位基因频率。综合所有结果,表明2个标记可在早期(种子或苗期)检测育种材料抗稻瘟病基因Pi2和Pita的等位基因型,无需将育种材料种植到病圃鉴定即可筛选出抗病单株。【结论】利用开发的KASP功能分子标记W-Pi2、W-Pita能够较好区分不同抗性的水稻亲本品种,可清楚区分水稻育种材料间不同的等位基因型,对育种材料进行准确筛选。