Optimization of spirocyclic proline tryptophan hydroxylase-1 inhibitors

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基于新颖S型转换函数的二进制粒子群优化算法求解具有单连续变量的背包问题

基于新颖S型转换函数的二进制粒子群优化算法求解具有单连续变量的背包问题

2021⁃02⁃10计算机应用,Journal of Computer Applications 2021,41(2):461-469ISSN 1001⁃9081CODEN JYIIDU http ://基于新颖S 型转换函数的二进制粒子群优化算法求解具有单连续变量的背包问题王泽昆,贺毅朝*,李焕哲,张发展(河北地质大学信息工程学院,石家庄050031)(∗通信作者电子邮箱heyichao@ )摘要:为了高效求解具有单连续变量的背包问题(KPC ),首先基于高斯误差函数提出了一个新颖S 型转换函数,给出了利用该转换函数将一个实向量转换为0-1向量的新方法,由此提出了一个新的二进制粒子群优化(NBPSO )算法;然后,利用KPC 的第二数学模型,并且把NBPSO 与处理KPC 不可行解的有效算法相结合,提出了求解KPC 的一个新方法。

为了检验NBPSO 求解KPC 的性能,利用NBPSO 求解四类大规模KPC 实例,并把所得计算结果与基于其他S 、V 型转换函数的二进制粒子群优化算法(BPSO )、具有混合编码的单种群二进制差分演化算法(S -HBDE )、具有混合编码的双种群二进制差分演化算法(B -HBDE )和二进制粒子群优化算法(BPSO )等的计算结果相比较。

比较结果表明NBPSO 不仅平均计算结果更优,而且稳定性更佳,说明NBPSO 的性能比其他算法有显著提升。

关键词:具有单连续变量的背包问题;组合优化问题;二进制粒子群优化算法;S 型转换函数中图分类号:TP18文献标志码:ABinary particle swarm optimization algorithm based on novel S-shape transferfunction for knapsack problem with a single continuous variableWANG Zekun ,HE Yichao *,LI Huanzhe ,ZHANG Fazhan(College of Information Technology ,Hebei GEO University ,Shijiazhuang Hebei 050031,China )Abstract:In order to solve the Knapsack Problem with a single Continuous variable (KPC )efficiently ,a novel S -shapetransfer function based on Gauss error function was proposed ,and a new approach of transforming a real vector into a 0-1vector by using the proposed transfer function was given ,thereby a New Binary Particle Swarm Optimization algorithm (NBPSO )was proposed.Then ,based on the second mathematical model of KPC and the combination of NBPSO and the effective algorithm to deal with the infeasible solutions of KPC ,a new approach to solve KPC was proposed.For validatingthe performance of NBPSO in solving KPS ,NBPSO was utilized to solve four kinds of large -scale KPC instances ,and the obtained calculation results were compared with those of Binary Particle Swarm Optimization algorithms (BPSOs )based on other S and V -shape transfer functions ,Single -population Binary Differential Evolution with Hybrid encoding (S -HBDE ),Bi -population Binary Differential Evolution with Hybrid encoding (B -HBDE )and Binary Particle Swarm Optimization algorithm (BPSO ).The comparison results show that NBPSO is superior to the comparison algorithms in average calculation result andstability ,illustrating that NBPSO has the performance better than other algorithms.Key words:Knapsack Problem with a single Continuous variable (KPC);combinatorial optimization problem;BinaryParticle Swarm Optimization (BPSO)algorithm;S -shape transfer function引言粒子群优化(Particle Swarm Optimization ,PSO )[1]是1995年由Kennedy 和Eberhart 提出的一种著名演化算法,具有结构简单、易于实现和计算成本低等优点,受到众多学者的关注和研究,已经在神经网络[2]、约束优化[3]、调度问题[4]等众多问题中得到了成功应用。

响应面优化β-环糊精对工业骨胶的除臭工艺

响应面优化β-环糊精对工业骨胶的除臭工艺

2017年第36卷第7期 CHEMICAL INDUSTRY AND ENGINEERING PROGRESS·2615·化 工 进展响应面优化β-环糊精对工业骨胶的除臭工艺刘静,苏秀霞,崔明(陕西科技大学化学与化工学院,陕西 西安 710021)摘要:研究了β-环糊精对骨胶除臭的最佳工艺条件。

以骨胶为原料,在单因素试验的基础上,以β-环糊精用量、反应时间、反应温度及搅拌速率为自变量,骨胶的气味、黏度为响应值,根据Box-Benhnken 试验设计原理,采用四因素三水平的分析法模拟得到二次多项式回归方程的预测模型,优化骨胶的除臭工艺。

回归模型具有高度显著性,方程对试验拟合较好,可以对骨胶的气味、黏度进行很好的分析和预测,得出各因素对气味、黏度的影响大小,响应面分析图表明搅拌速率和反应时间的相互作用对黏度的影响显著;结果显示骨胶除臭的最佳优化条件为β-环糊精的用量为骨胶质量的6.3%,反应温度45.7℃,反应时间为90min ,搅拌速率300r/min ;优化后的骨胶气味良好,黏度为8.9Pa·s ;实测值与预测值的相对误差较小,说明最佳优化工艺的可靠性较高。

关键词:骨胶;响应面;β-环糊精;除臭中图分类号:TQ431.5 文献标志码:A 文章编号:1000–6613(2017)07–2615–06 DOI :10.16085/j.issn.1000-6613.2016-2262Optimization on deodorization of β-cyclodextrin in industrial boneglue by response surfaceLIU Jin g ,SU Xiuxia ,CUI Ming(College of Chemistry and Chemical Engineering ,Shaanxi University of Science & Technology ,Xi’an 710021,Shaanxi ,China )Abstract :The objective of this study is to optimize deodorization conditions for industrial bone glue with β-cyclodextrin. Residues from bone glue were used in this paper. On the basis of single-factor test ,the response surface methodology was utilized to investigate the effects of β-cyclodextrin content ,reaction time ,reaction temperature and stirring rate. The odor and viscosity of the bone glue were chosen as response value. The regression model had a high significance level ,and the established regression equations fit with experimental results well and showed good prediction for the odor and viscosity value of bone glue. The influences of each factor on the smell and viscosity were obtained. The response surface plots showed that the interaction between stirring rate and reaction time was outstanding. The results showed that the optimal conditions for deodorization of β-cyclodextrin content was 6.3% of bone glue amount ,reaction temperature of 45.7℃,reaction time of 90min ,stirring rate of 300r/min. The bone glue after optimized deodorization process had a good smell and a viscosity of 8.9 Pa·s. The relative error between the measured value and the predicted value is small ,which indicates that the reliability of the optimal optimization process is high.Key words :bone glue ;response surface ;β-cyclodextrin ;deodorization近些年来,纸张和木材行业的大力发展,使得胶黏剂的使用量大大增加。

滚动轴承故障诊断的改进小波变换谱峭度法

滚动轴承故障诊断的改进小波变换谱峭度法
关键词 : 滚动轴承 ; 故障诊断 ; r t Mol 小波变换 ; e 谱峭度
中 图分 类 号 :H13 3 ;N 1 . T 3 .3 T 9 17 文 献 标 志 码 : B 文 章 编 号 :00— 7 2 2 1 ) 8— 0 6— 4 10 3 6 (0 0 0 0 4 0
I p o e S c r lK u t ssAl o ih s d o a ee a so m a i n m r v d pe t a r o i g rt m Ba e n W v ltTr n f r to f r Roln a i g Fa tDig o i o l g Be rn ul a n ss i
器的基础上进行 的, 要真正找 到最优滤波器仍需
在此 基础 上再 比较更 多 组 滤 波器 组 , 程很 繁 琐 。 过
考虑 到谱 峭度 与构造 最 优 匹 配滤 波 器之 间 有着 密 切 的关 系 Ij在 总结 前人 研 究 的 基础 上 , 谱 峭 4, 将 度构 造最优 滤 波器 的特 性 与基 于 M r t ol 小波 分 解 e
求谱 峭度 的方 法结 合 起来 , 由此 取 得 了 比原 方 并 法更 加优 良的检测 和诊 断性能 。
M rt o e复小波滤波器 , l 利用此滤波器对原始信号的
滤 波结果 , 到 包 络 分 析 结 果 , 终 检 测 故 障频 得 最 率 。需要 指出 的是 由此法 得 到 的最 大 谱 峭度 是 出 现在 每倍频 程六 个滤 波器 的条件 下 , 比文 献 [ ] 1 采 用 的五个滤 波器 的情况 结果 更优 。
因此文献[ ] 出了基 于谱峭度的改进包络分析 1提 法, 此方法有效地实现 了符合最佳包络 的频带 的 自动检测 , 在实 际工作 中得 到 了检 验 , 并 但是 其 只 研 究 了一 组 Mol 复小 波 滤 波 器 组 , 有 深 入 研 rt e 没

响应曲面法优化磷脂酶Lecitase Ultra 用于茶油脱胶工艺的研究

响应曲面法优化磷脂酶Lecitase Ultra 用于茶油脱胶工艺的研究

3262008, Vol.29, No.09食品科学※工艺技术响应曲面法优化磷脂酶 Lecitase Ultra 用于茶油脱胶工艺的研究李 晶 1 ,胡婕伦 1 ,谢明勇 1, * ,聂少平 1 ,张 彬 1 ,JULIA Goh su-yun 2(1.南昌大学 食品科学与技术国家重点实验室, 江西 南昌 2.淡马锡理工学院应用科学系,新加坡 529757) 330047;摘 要:本研究在单因素试验的基础上,利用 Box-Benhnken 的中心组合试验设计及响应面分析法优化磷脂酶 Lecitase Ultra 对茶油脱胶的工艺,建立反应时间、反应温度、酶用量与磷脂脱除率之间的数学模型,确立酶法对茶油脱 胶的最佳工艺条件,即反应时间 4.3h、反应温度 47℃、酶用量 44mg/kg。

在此最佳工艺条件下,茶油脱胶率达 89.41%(n=3)。

关键词:茶油;脱胶;磷脂酶;响应曲面法Optimization of Degumming Process of Camellia Oil with Phospholipase Using Response Surface MethodologyLI Jing1,HU Jie-lun1,XIE Ming-yong1,*, NIE Shao-ping1,ZHANG Bin1,JULIA Goh su-yun2 (1. State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang 330047, China; 2. School of Applied Science, Temasek Polytechnic, Singapore 529757, Singapore) Abstract: On the basis of single-factor tests, the degumming conditions of camellia oil by enzyme method were optimized through Box-Benhnken design and response surface methodology (RSM). The mathematical-regression model was established about the dependent variable (degumming rate of camellia oil) and independent variables (time, temperature and phospholipase Lecitase Ultra dosage). According to this model, the optimum degumming conditions are determined as time 4.3 h, temperature 47 ℃ and phospholipase Lecitase Ultra dosage 44 mg/kg. The response value (degumming rate) for these optimum values is 89.41% (n=3). Key words: camellia oil;degumming;phospholipase;response surface methodology 中图分类号:R284.2 文献标识码:A 文章编号:1002-6630(2008)09-0326-05茶油是我国特有的木本食用植物油,其脂肪酸组成 与世界上公认的最好的植物油脂橄榄油相似,有“东 方橄榄油”之美称[1]的磷脂分为水化磷脂和非水化磷脂两类 [2] 。

基于非劣排序遗传算法的三代轮毂轴承多目标优化

基于非劣排序遗传算法的三代轮毂轴承多目标优化
数 处 理 约 束 条件 , 将 原 约 束优 化 问 题 转 化 为 极 小 化 的 无 约 束 优 化 问题 , 采 用 带 精 英 保 留 策 略 的 非 劣 排 序 遗 传 算 法( NS GA - I I ) 进 行 轮 毂 轴 承 多 目标 优 化 。通 过 有 限 元 仿 真 对 优 化 前 后 的 结 构 应 力 情 况 进 行 了对 比 。 分 析 结 果 表明 : 在满足规定约束的条件下 , 提 出 的优 化 方 案 实现 了 3个 目标 函数 整 体 性 能 的 同时提 升 ; 内法 兰 、 外法兰、 内 圈、 滚 珠 的 应 力 集 中情 况 均 有 改 善 , 模 型整 体 最 大等 效 应 力较 优 化 前 降低 8 . 6 1 。 关键词 : 轮毂轴承 ; 多 目标优 化 ; 非 劣排 序 遗 传 算 法 ; 有 限元
W h e e l Hu b Be a r i ng Ba s e d o n NSGA— I I
Li n Fe n ,W an g We i , Zhan g Yao we n ,Zh u We i we n ( C o l l e g e o f En e r g y a n d Po we r En g i n e e r i n g.Na n j i n g Un i v e r s i t y o f Ae r o n a u t i c s& As t r o n a u t i c s ,Na n j i n g,2 1 0 0 1 6 ,Ch i n a )
中图分类号 : U4 6 3 . 2 文献标志码 : A 文章编号 : 1 0 0 5 — 2 6 1 5 ( 2 0 1 3 ) 0 6 — 0 8 6 5 — 0 6
Mu l t i - o b j e c t i v e Op t i mi z a t i o n f o r T h i r d G e n e r a t i o n

基于奇异值分解强跟踪滤波的机车黏着系数估计

基于奇异值分解强跟踪滤波的机车黏着系数估计
的 大 小 , 示 在 一 定 轴 重 的 垂 向 静 载 荷 N 下 动 轮 所 能 表 获 得轮 周切 向牵 引力 F 的 大 小 。 当 F 取 得 最 大 值 时 ,
黏 着 系数进 行 准确 地 估计 。 目前 一些 学 者 提 出 了黏 着
系数 的估 计 方 法 , 献 [ ] 于 线性 系统 理论 设 计 了二 文 1基
跟 踪 滤 波 算 法 由 于 引 入 了 渐 消矩 阵 , 递 推 更 新 预 测 误 差 方 差 阵 时变 成 不对 称 , 能 导 致 滤 波 发 散 的 现 象 , 究 了 在 可 研 基 于奇 异值 分 解 的 更 新 算 法 , 证 其 收 敛 性 。利 用 改 进 算 法 在 线 估 计 机 车 运 行 过 程 中 的 干 扰 转 矩 , 而 估 计 黏 着 保 进
机 车车 轮与钢 轨 之 问的 相互 作 用 情 况 由黏 着 特 性
描述 , 黏着特 性 曲线 决 定 了轮 轨 问 的接 触 力 传 递 机 制 , 只有处 于黏着 状态 才会 产生 黏着力 , 而 产生 使机 车 前 进
进 的动力 。用 黏着 系数 一 F / 表 征黏 着产 生牵 引力 X
方程 ( ) , 为 牵 引 电机 转 矩 ; 3中 T
为 牵 引 电机 角 速
用 的最 大牵 引力 , 要 对 其 进 行 优 化黏 着 控 制 , 需 有效 的 方法 是搜 索 并逼 近 当前 的最大 黏着 系数 , 牵 引力 保 持 使 在最 大 黏着 系数 的 临界点 上 , 而确 定 可实 现 最 大牵 引 从
过程 的数 值稳 定 性 。最 后 进行 机 车 黏 着 系 数 的 在 线 估 计, 仿真结 果 表 明 , 提方 法能 够跟 踪 黏着 特性 的变 化 , 所

粒子群算法可再生能源优化配置

粒子群算法可再生能源优化配置

粒子群算法可再生能源优化配置英文回答:Particle swarm optimization (PSO) is a popular optimization algorithm that is widely used in various fields, including renewable energy optimization. PSO is inspired by the social behavior of bird flocking or fish schooling, where a group of particles (representing potential solutions) move through a search space to find the optimal solution.In the context of renewable energy optimization, the goal is to find the optimal configuration of renewable energy sources such as solar panels or wind turbines. This involves determining the number, size, and placement of these sources to maximize energy generation while considering constraints such as cost, land availability, and environmental impact.PSO works by iteratively updating the position andvelocity of each particle based on its own best solution and the best solution found by the entire swarm. Each particle "flies" through the search space, adjusting its position and velocity based on its own experience and the influence of the best solution found so far. This allows the particles to explore the search space efficiently and converge towards the optimal solution.For example, let's say we want to optimize the placement of solar panels on a rooftop to maximize energy generation. We can represent each potential placement as a particle in the PSO algorithm. The position of eachparticle represents the coordinates of the solar panels on the rooftop, and the velocity represents the speed and direction of movement.During each iteration, each particle updates its position and velocity based on its own best solution (i.e., the configuration that generated the most energy so far) and the best solution found by the entire swarm. The particle adjusts its position by considering its current velocity and the influence of the best solution. Over time,the particles move towards the optimal configuration that maximizes energy generation.中文回答:粒子群算法(Particle Swarm Optimization,PSO)是一种广泛应用于各个领域的优化算法,包括可再生能源优化配置。

英语论文的标题特征

英语论文的标题特征

硕士英语—学术论文写作
标题中所用的词尽量使用表达全文内容的关 键词,论文当中的中心词要能够在标题中体 现出来。
1、A parametric study of copper deposition in a fluidized bed electrode
2、Modification of the packed-bed electrode and its potential distribution
3、Electrolytic recovery of metals from waste waters with the Swiss-roll cell
4 、 Electrochemical oxidation of phenol at boron-doped diamond electrode
论文标题中最常出现的是介词,例如of、by、with、in、 on等,每个介词代表的含义不同 1、of频率最高,通常以名词所有格表示逻辑上的主宾关系
硕士英语—学术论文写作
需要探讨的问题:
一、用using连接的标题
1、Adsorption of lead using a new green material
obtained from Portulaca plant
2、Denitrification
of
nitrate-contaminated
groundwater using biodegradable snack ware as
Treatment of Landfill Leachate by Fenton Process with Nano sized Zero Valent Iron particles 2、by和with表示研究的方法和工具

顺序形态滤波与样本熵在转子故障特征提取中的应用

顺序形态滤波与样本熵在转子故障特征提取中的应用
别 采 样 ,各 取 1 0 组 数 据 。 先 选 取 直 线 结 构 元 素 B= { 0 , 0 , 0 } ,采 用 式 ( 3 )构 造 的顺 序 组 合 形 态 滤 波器 将 原 始 信 号 进 行 降 噪 预 处理 以 消 除噪 声 的影 响 ,然 后根 据式 ( 9 )计算 滤 波 后信 号 的样 本熵 。
张文斌
ZHANG W e n . b i n
Байду номын сангаас
( 红河学院 工 学院,蒙 自 6 6 1 1 O 0 )

要 :结合顺序形态滤波方法 与非 线性动力学参数样本熵 , 提 出一种新的转子故障特征提取方法 。首 先引入 循环统 计学 的思 想对传 统形态滤 波方 法进行 改进 ,定 义了顺序 形态滤 波器 ,并 结合实 际选用 最简单 的直线结 构元素 ,对实测 转子 振动信号 进行顺序 形态滤 波降噪预 处理 ;然后计 算降 噪后信号 的样本熵 ,包括 转子正 常 、不 平衡 、不 对中 、油膜涡动 和碰摩 等五种工 况的振 动信号 ;最后将 样本熵 作为特征 ,依 据不 同的故障对应 不 同的样本熵 分布 ,对各种故 障状态 进行评价 . 转子系统故障识别的实例验证 了该方法的可行性和有效性 。
号 ,结 构 元素 的长 度取3 。

B )
显 然 ,样 本熵 的值 与参 数 I T I 和r 的取 值 有 关 。
参 考 文献 [ 4 1 关 于 参数 I T I 和r 不 同取 值 对 样 本熵 的影 响 ,对 实 际转子 振动 信号 估计 样本 熵时 ,取m= 2 和 r = 0 . 2 进行 计算 。
量 的 噪 声干 扰 而 无 法 准 确 反 映 故 障 特 征 的 问 题 ,

Optimizing spatio-temporal filters for improving BCI

Optimizing spatio-temporal filters for improving BCI

Guido Dornhege1,Benjamin Blankertz1,Matthias Krauledat1,3,Florian Losch2,Gabriel Curio2and Klaus-Robert Müller1,31Fraunhofer FIRST.IDA,Kekuléstr.7,12489Berlin,Germany2Campus Benjamin Franklin,CharitéUniversity Medicine Berlin,Hindenburgdamm30,12203Berlin,Germany.3University of Potsdam,August-Bebel-Str.89,14482Germany{dornhege,blanker,kraulem,klaus}@first.fhg.de,{florian-philip.losch,gabriel.curio}@charite.deAbstractBrain-Computer Interface(BCI)systems create a novel communicationchannel from the brain to an output device by bypassing conventionalmotor output pathways of nerves and muscles.Therefore they couldprovide a new communication and control option for paralyzed patients.Modern BCI technology is essentially based on techniques for the clas-sification of single-trial brain signals.Here we present a novel techniquethat allows the simultaneous optimization of a spatial and a spectralfilterenhancing discriminability of multi-channel EEG single-trials.The eval-uation of60experiments involving22different subjects demonstratesthe superiority of the proposed algorithm.Apart from the enhanced clas-sification,the spatial and/or the spectralfilter that are determined by thealgorithm can also be used for further analysis of the data,e.g.,for sourcelocalization of the respective brain rhythms.1IntroductionBrain-Computer Interface(BCI)research aims at the development of a system that allows direct control of,e.g.,a computer application or a neuroprosthesis,solely by human in-tentions as reflected in suitable brain signals,cf.[1,2,3,4,5,6,7,8,9].We will be focussing on noninvasive,electroencephalogram(EEG)based BCI systems.Such devices can be used as tools of communication for the disabled or for healthy subjects that might be interested in exploring a new path of man-machine interfacing,say when playing BCI operated computer games.The classical approach to establish EEG-based control is to set up a system that is con-trolled by a specific EEG feature which is known to be susceptible to conditioning and to let the subjects learn the voluntary control of that feature.In contrast,the Berlin Brain-Computer Interface(BBCI)uses well established motor competences in control paradigms and a machine learning approach to extract subject-specific discriminability patterns from high-dimensional features.This approach has the advantage that the long subject training needed in the operant conditioning approach is replaced by a short calibration measurement(20minutes)and machine training(1minute).The machine adapts to the specific charac-teristics of the brain signals of each subject,accounting for the high inter-subject variability. With respect to the topographic patterns of brain rhythm modulations the Common Spatial Patterns(CSP)(see[10])algorithm has proven to be very useful to extract subject-specific, discriminative spatialfilters.On the other hand the frequency band on which the CSP al-gorithm operates is either selected manually or unspecifically set to a broad bandfilter,cf. [10,5].Obviously,a simultaneous optimization of a frequencyfilter with the spatialfilter is highly desirable.Recently,in[11]the CSSP algorithm was presented,in which very simple frequencyfilters(with one delay tap)for each channel are optimized together with the spatialfilters.Although the results showed an improvement of the CSSP algorithm over CSP,theflexibility of the frequencyfilters is very limited.Here we present a method that allows to simultaneously optimize an arbitrary FIRfilter within the CSP analysis.The pro-posed algorithm outperforms CSP and CSSP on average,and in cases where a separation of the discriminative rhythm from dominating non-discriminative rhythms is of importance,a considerable increase of classification accuracy can be achieved.2Experimental SetupIn this paper we investigate data from60EEG experiments with22different subjects.All experiments included so called training sessions which are used to train subject-specific classifiers.Many experiments also included feedback sessions in which the subject could steer a cursor or play a computer game like brain-pong by BCI control.Data from feedback sessions are not used in this a-posteriori study since they depend on an intricate interaction of the subject with the original classification algorithm.In the experimental sessions used for the present study,labeled trials of brain signals were recorded in the following way:The subjects were sitting in a comfortable chair with arms lying relaxed on the armrests.All4.5–6seconds one of3different visual stimuli indicated for3–3.5seconds which mental task the subject should accomplish during that period.The investigated mental tasks were imagined movements of the left hand(l),the right hand (r),and one foot(f).Brain activity was recorded from the scalp with multi-channel EEG amplifiers using32,64resp.128channels.Besides EEG channels,we recorded the elec-tromyogram(EMG)from both forearms and the leg as well as horizontal and vertical elec-trooculogram(EOG)from the eyes.The EMG and EOG channels were used exclusively to make sure that the subjects performed no real limb or eye movements correlated with the mental tasks that could directly(artifacts)or indirectly(afferent signals from muscles and joint receptors)be reflected in the EEG channels and thus be detected by the classifier, which operates on the EEG signals only.Between120and200trials for each class were recorded.In this study we investigate only binary classifications,but the results can be expected to safely transfer to the multi-class case.3Neurophysiological BackgroundAccording to the well established model called homunculus,first described by[12],for each part of the human body there exists a corresponding region in the motor and so-matosensory area of the neocortex.The’mapping’from the body to the respective brain areas preserves in big parts topography,i.e.,neighboring parts of the body are almost rep-resented in neighboring parts of the cortex.While the region of the feet is located at the center of the vertex,the left hand is represented lateralized on the right hemisphere and the right hand on the left hemisphere.Brain activity during rest and wakefulness is describable by different rhythms located over different brain areas.These rhythms reflect functional states of different neuronal cortical networks and can be used for brain-computer inter-facing.These rhythms are blocked by movements,independent of their active,passive or reflexive origin.Blocking effects are visible bilaterally but pronounced contralaterally in the cortical area that corresponds to the moved limb.This attenuation of brain rhythms isd B Figure 1:The plot shows the spectra for one subject during left hand (light line)and foot (dark line)motor imagery be-tween 5and 25Hz at scalp positions Pz,Cz and C4.In both central channels two peaks,one at 8Hz and one at 12Hz are visible.Below each channel the r 2-value which measures discriminability is added.It indicates that the secondpeak contains more discriminative infor-mation.termed event-related desynchronization (ERD),see [13].Over sensorimotor cortex a so called idle-or µ-rhythm can be measured in the scalp EEG.The most common frequency band of µ-rhythm is about 10Hz (precentral α-or µ-rhythm,[14]).Jasper and Penfield ([12])described a strictly local so called beta-rhythm about 20Hz over human motor cor-tex in electrocorticographic recordings.In Scalp EEG recording one can find µ-rhythm over motor areas mixed and superimposed by 20Hz-activity.In this context µ-rhythm is sometimes interpreted as a subharmonic of cortical faster activity.These brain rhythms de-scribed above are of cortical origin but the role of a thalomo-cortical pacemaker has been discussed since the first description of EEG by Berger ([15])and is still a point of dis-cussion.Lopes da Silva ([16])showed that cortico-cortical coherence is much larger than thalamo-cortical.However,since the focal ERD in the motor and/or sensory cortex can be observed even when a subject is only imagining a movement or sensation in the specific limb,this feature can well be used for BCI control.The discrimination of the imagina-tion of movements of left hand vs.right hand vs.foot is based on the topography of the attenuation of the µand/or βrhythm.There are two problems when using ERD features for BCI control:(1)The strength of the sensorimotor idle rhythms as measured by scalp EEG is known to vary strongly between subjects.This introduces a high intersubject variability on the accu-racy with which an ERD-based BCI system works.There is another feature independent from the ERD reflecting imagined or intended movements,the movement related potentials (MRP),denoting a negative DC shift of the EEG signals in the respective cortical regions.See [17,18]for an investigation of how this feature can be exploited for BCI use and combined with the ERD feature.This combination strategy was able to greatly enhance classification performance in offline studies.In this paper we focus only on improving the ERD-based classification,but all the improvements presented here can also be used in the combined algorithm.(2)The precentral µ-rhythm is often superimposed by the much stronger posterior α-rhythm,which is the idle rhythm of the visual system.It is best articulated with eyes closed,but also present in awake and attentive subjects,see Fig.1at channel Pz.Due to volume conduction the posterior α-rhythm interferes with the precentral µ-rhythm in the EEG channels over motor cortex.Hence a µ-power based classifier is susceptible to mod-ulations of the posterior α-rhythm that occur due to fatigue,change in attentional focus while performing tasks,or changing demands of visual processing.When the two rhythms have different spectral peaks as in Fig.1,channels Cz and C4,a suitable frequency filter can help to weaken the interference.The optimization of such a filter integrated in the CSP algorithm is addressed in this paper.4Spatial Filter -the CSP AlgorithmThe common spatial pattern (CSP)algorithm ([19])is very useful in calculating spatial filters for detecting ERD effects ([20])and for ERD-based BCIs,see [10],and has been extended to multi-class problems in [21].Given two distributions in a high-dimensional space,the (supervised)CSP algorithm finds directions (i.e.,spatial filters)that maximizevariance for one class and at the same time minimize variance for the other class.Afterhaving band-passfiltered the EEG signals to the rhythms of interest,high variance reflects a strong rhythm and low variance a weak(or attenuated)rhythm.Let us take the exampleof discriminating left hand vs.right hand imagery.According to Sec.3,the spatialfilterthat focusses on the area of the left hand is characterized by a strong motor rhythm during imagination of right hand movements(left hand is in idle state),and by an attenuated motorrhythm during left hand imagination.This criterion is exactly what the CSP algorithm optimizes:maximizing variance for theclass of right hand trials and at the same time minimizing variance for left hand trials. Furthermore the CSP algorithm calculates the dualfilter that will focus on the area of theright hand(and it will even calculate severalfilters for both optimizations by consideringorthogonal subspaces).The CSP algorithm is trained on labeled data,i.e.,we have a set of trials s i,i=1,2,...,where each trial consists of several channels(as rows)and time points(as columns).Aspatialfilter w∈I R#channels projects these trials to the signalˆs i(w)=w⊤s i with only one channel.The idea of CSP is tofind a spatialfilter w such that the projected signal hashigh variance for one class and low variance for the other.In other words we maximize thevariance for one class whereas the sum of the variances of both classes remains constant,which is expressed by the following optimization problem:max w∑i:Trial in Class1var(ˆs i(w)),s.t.∑ivar(ˆs i(w))=1,(1)where var(·)is the variance of the vector.An analoguous formulation can be formed for the second class.Using the definition of the variance we simplify the problem tomaxww⊤Σ1w,s.t.w⊤(Σ1+Σ2)w=1,(2)whereΣy is the covariance matrix of the trial-concatenated matrix of dimension[channels ×concatenated time-points]belonging to the respective class y∈{1,2}.Formulating the dual problem we canfind that the problem can be solved by calculating a matrix Q and diagonal matrix D with elements in[0,1]such thatQΣ1Q⊤=D and QΣ2Q⊤=I−D(3) and by choosing the highest and lowest eigenvalue.Equation(3)can be accomplished in the following way.First we whiten the matrixΣ1+Σ2, i.e.,determine a matrix P such that P(Σ1+Σ2)P⊤=I which is possible due to positive definiteness ofΣ1+Σ2.Then defineˆΣy=PΣy P⊤and calculate an orthogonal matrix R and a diagonal maxtrix D by spectral theory such thatˆΣ1=RDR⊤.ThereforeˆΣ2=R(I−D)R⊤sinceˆΣ1+ˆΣ2=I and Q:=R⊤P satisfies(3).The projection that is given by the j-th row of matrix R has a relative variance of d j(j-th element of D)for trials of class1and relative variance1−d j for trials of class2.If d j is near1thefilter given by the j-th row of R maximizes variance for class1,and since1−d j is near0,minimizes variance for class2.Typically one would retain some projections corresponding to the highest eigenvaluesd j,i.e.,CSPs for class1,and some corresponding to the lowest eigenvalues,i.e.,CSPs for class2.5Spectral FilterAs discussed in Sec.3the content of discriminative information in different frequency bands is highly subject-dependent.For example the subject whose spectra are visualized in Fig.1shows a highly discriminative peak at12Hz whereas the peak at8Hz does not show good discrimination.Since the lower frequency peak is stronger a better performance inclassification can be expected,if we reduce the influence of the lower frequency peak for this subject.However,for other subjects the situation looks differently,i.e.,the classifica-tion might fail if we exclude this information.Thus it is desirable to optimize a spectral filter for better discriminability.Here are two approaches to this task.CSSP.In[11]the following was suggested:Given s i the signal sτi is defined to be the signal s i delayed byτtimepoints.In CSSP the usual CSP approach is applied to the concatenation of s i and sτi in the channel dimension,i.e.,the delayed signals are treated as new channels. By this concatenation step the ability to neglect or emphasize specific frequency bands can be achieved and strongly depends on the choice ofτwhich can be accomplished by some validation approach on the training set.More complex frequencyfilters can be found by concatenating more delayed EEG-signals with several delays.In[11]it was concluded that in typical BCI situations where only small training sets are available,the choice of only one delay tap is most effective.The increasedflexibility of a frequencyfilter with more delay taps does not trade off the increased complexity of the optimization problem.CSSSP.The idea of our new CSSSP algorithm is to learn a complete global spatial-temporalfilter in the spirit of CSP and CSSP.A digital frequencyfilter consists of two sequences a and b with length n a and n b such that the signal x isfiltered to y bya(1)y(t)=b(1)x(t)+b(2)x(t−1)+...+b(n b)x(t−n b−1)−a(2)y(t−1)−...−a(n a)y(t−n a−1)Here we restrict ourselves to FIR(finite impulse response)filters by defining n a=1and a=1.Furthermore we define b(1)=1andfix the length of b to some T with T>1.By this restriction we resign someflexibility of the frequencyfilter but it allows us tofind a suitable solution in the following way:We are looking for a real-valued sequence b1,...,T with b(1)=1such that the trialss i,b=s i+∑τ=2,...,Tbτsτi(4) can be classified better in some ing equation(1)we have to solve the problemmax w,b,b(1)=1∑i:Trial in Class1var(ˆs i,b(w)),s.t.∑ivar(ˆs i,b(w))=1,(5)which can be simplified tomax b,b(1)=1maxww⊤ ∑τ=0,...,T−1 ∑j=1,...,T−τb(j)b(j+τ) Στ1 w,s.t.w⊤ ∑τ=0,...,T−1 ∑j=1,...,T−τb(j)b(j+τ) (Στ1+Στ2) w=1.(6)whereΣτy=E( s i(sτi)⊤+sτi s⊤i|i:Trial in Class y ),namely the correlation between the signal and the byτtimepoints delayed signal.Since we can calculate for each b the optimal w by the usual CSP techniques(see equation (2)and(3))a(T−1)-dimensional(b(1)=1)problem remains which we can solve with usual line-search optimization techniques if T is not too large.Consequently we get for each class a frequency bandfilter and a pattern(or similar to CSP more than one pattern by choosing the next eigenvectors).However,with increasing T the complexity of the frequencyfilter has to be controlled in order to avoid overfitting.This control is achieved by introducing a regularization term in510152025−20−10010Frequency (Hz)M a g n i t u d e (d B )Figure 2:The plot on the left shows one learned frequency filter for the subject whose spectra was shown Fig.1.In the plot on the right the resulting spectra are visualized after applying the frequency filter on the left.By this technique the classification error could be reduced from 12.9%to 4.3%.the following way:max b ,b (1)=1maxw w ⊤ ∑τ=0,...,T −1∑j =1,...,T −τb (j )b (j +τ)Στ1 w −C /T ||b ||1,s .t .w ⊤ ∑τ=0,...,T −1 ∑j =1,...,T −τb (j )b (j +τ) (Στ1+Στ2) w =1.(7)Here C is a non-negative regularization constant,which has to be chosen,e.g.,by cross-validation.Since a sparse solution for b is desired,we use the 1-norm in this formulation.With higher C we get sparser solutions for b until at one point the usual CSP approach remains,i.e.,b (1)=1,b (m )=0for m >1.We call this approach Common Sparse Spectral Spatial Pattern (CSSSP)algorithm.6Feature Extraction,Classification and Validation6.1Feature ExtractionAfter choosing all channels except the EOG and EMG and a few of the outermost channels of the cap we apply a causal band-pass filter from 7–30Hz to the data,which encompasses both the µ-and the β-rhythm.For classification we extract the interval 500–3500ms after the presented visual stimulus.To these trials we apply the original CSP ([10])algorithm (see Sec.4),the extended CSSP ([11]),and the proposed CSSSP algorithm (see Sec.5).For CSSP we choose the best τby leave-one-out cross validation on the training set.For CSSSP we present the results for different regularization constants C with fixed T =16.Here we use 3patterns per class which leads to a 6-dimensional output signal.As a measure of the amplitude in the specified frequency band we calculate the logarithm of the variances of the spatio-temporally filtered output signals as feature vectors.6.2Classification and ValidationThe presented preprocessing reduces the dimensionality of the feature vectors to six.Since we have 120up to 200samples per class for each data set,there is no need for regulariza-tion when using linear classifiers.When testing non-linear classification methods on these features,we could not observe any statistically significant gain for the given experimen-tal setup when compared to Linear Discriminant Analysis (LDA)(see also [22,6,23]).Therefore we choose LDA for classification.For validation purposes the (chronologically)first half of the data are used as training and the second half as test data.7ResultsFig.2shows one chosen frequency filter for the subject whose spectra are shown in Fig.1and the remaining spectrum after using this filter.As expected the filter detects that thereCSPvs.CSSSPCSSPvs.CSSSPFigure3:Each plots shows validation error of one algorithm against another,in row1that is CSP (y-axis)vs.CSSSP(x-axis),in row2that is CSSP(y-axis)vs.CSSSP(x-axis).In columns the regularization parameter of CSSSP is varied between0.1,0.5,1and5.In each plot a cross above the diagonal marks a dataset where CSSSP outperforms the other algorithm.is a high discrimination in frequencies at12Hz,but only a low discrimination in the fre-quency band at8Hz.Since the lower frequency peak is very predominant for this subject without having a high discrimination power,afilter is learned which drastically decreases the amplitude in this band,whereas full power at12Hz is retained.Applied to all datasets and all pairwise class combinations of the datasets we get the results shown in Fig.3.Only the results of those datasets are displayed whose classification accu-racy exceeds70%for at least one classifier.First of all,it is obvious that a small choice of the regularization constant is problematic,since the algorithm tends to overfit.For high values CSSSP tends towards the CSP performance since using frequencyfilters is punished too hard.In between there is a range where CSSSP is better than CSP.Furthermore there are some datasets where the gain by CSSSP is huge.Compared to CSSP the situation is similar,namely that CSSSP outperforms the CSSP in many cases and on average,but there are also a few cases,where CSSP is better.An open issue is the choice of the parameter C.If we choose it constant at1for all datasets thefigure shows that CSSSP will typically outperform pared to CSSP both cases appear,namely that CSSP is better than CSSSP and vice versa.A more refined way is to choose C individually for each dataset.One way to accomplish this choice is to perform cross-validations for a set of possible values of C and to select the C with minimum cross-validation error.We have done this,for example,for the dataset whose spectra are shown in Fig.1.Here on the training set for C the value0.3is chosen. The classification error of CSSSP with this C is4.3%,whereas CSP has12.9%and CSSP 8.6%classification error.8Concluding discussionIn past BCI research the CSP algorithm has proven to be very sucessful in determining spatialfilters which extract discriminative brain rhythms.However the performance can suffer when a non-discriminative brain rhythm with an overlapping frequency range inter-feres.The presented CSSSP algorithm successful solves such problematic situations by optimizing simultaneously with the spatialfilters a spectralfilter.The trade-off between flexibility of the estimated frequencyfilter and the danger of overfitting is accounted for by a sparsity constraint which is weighted by a regularization constant.The successfulness of the proposed algorithm when compared to the original CSP and to the CSSP algorithm was demonstrated on a corpus of60EEG data sets recorded from22different subjects.Acknowledgments We thank S.Lemm for helpful discussions.The studies were supported by 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轴流式叶轮轴四轴加工刀具轨迹优化

轴流式叶轮轴四轴加工刀具轨迹优化

Value Engineering———————————————————————基金项目:安徽省高等学校省级质量工程项目“智能制造技术教学团队”(2020jxtd025);安徽省高等学校省级质量工程项目“示范基层教学组织(智能制造教研室)”(2020SJSFJXZZ041);安徽省高等学校省级质量工程项目“装备制造类专业现代学徒制”(2021xdxtz007);安徽高校自然科学研究项目“数控设备故障智能诊断与监测实训平台研发”(KJ2021A1489)。

作者简介:刘辉(1990-),男,安徽蚌埠人,讲师,机械工程硕士,主要从事机械制造与自动化技术研究。

0引言叶轮是透平机的关键零件,在风机、涡轮增压器、航空发动机等高端装备上广泛使用。

根据能量转换的不同,既可作压气机,也可作动力机[1]。

工作时叶轮高速旋转,民用涡轮风扇发动机叶轮转速在几千到几万之间,车用涡轮增压器的小直径叶轮转速可达每分钟20万转。

高转速条件下的摩擦抑制、温度控制、压力损失、做功效率对叶轮的加工制造环节提出了较高要求。

一直以来,叶轮首选的加工方案采用五轴联动数控加工中心实现轮毂和叶片的粗精加工,这种做法对设备投入要求较高,叶片的制造成本难以降低。

固然五轴联动加工能够获得优良的外形、尺寸精度和表面质量,但并非所有的叶轮都必须应用五轴加工策略,针对部分情况下轴流式叶轮的制造需求,四轴联动加工方法同样适用。

四轴联动数控加工中心因其来源于三轴数控机床增加第四轴,制造和使用成本相对较低,虽然刀具的自由度存在局限,若能在工艺和编程方面扬长避短,不仅能顺利完成轴流式叶轮的铣削加工,同时在生产成本上能够积累一定的优势。

本文提出了一种基于UG NX 软件的针对某轴流式叶轮轴的四轴联动优化加工方法,并实现了上机验证。

1轴流式叶轮轴流式叶轮工作时流体从轴向流入并沿轴向流出,广泛应用于风机、压气机、涡轮机等机构。

以气体流体为例,利用高速旋转可以对气体做功实现多级压缩,也可以将高温燃气的内能转化为机械能,吹动叶轮高速旋转。

关于粒子群优化的英文摘要范文

关于粒子群优化的英文摘要范文

关于粒子群优化的英文摘要范文Particle Swarm Optimization: An Evolutionary Approach to Optimization.Particle Swarm Optimization (PSO) is an optimization technique that模拟 the social behavior of bird flocking or fish schooling. This algorithm is based on the observation that individuals in a group tend to move towards the best performing individuals in the group, thereby improvingtheir own performance. PSO has been widely used in various fields such as engineering, finance, and machine learning due to its simplicity, efficiency, and ease of implementation.The basic idea behind PSO is to initialize a population of particles randomly in the search space. Each particle represents a potential solution to the optimization problem and is characterized by its position and velocity. The position of each particle is updated based on its own best position (pbest) and the best position found by anyparticle in the swarm (gbest). The velocity of eachparticle is adjusted based on the difference between its current position and the best positions, which effectively guides the particles towards the optimal solution.One of the main challenges in PSO is the balance between exploration and exploitation. Exploration refers to the ability of the algorithm to search for new regions in the search space, while exploitation refers to the abilityto refine the current best solution. To address this challenge, various modifications and extensions of PSO have been proposed, including the introduction of inertia weight, velocity clamping, and cognitive and social acceleration coefficients.In this paper, we propose a novel PSO variant that incorporates adaptive and time-varying inertia weights. The inertia weight controls the influence of the previous velocity on the current velocity update. By adapting the inertia weight based on the progress of the algorithm, we aim to improve the exploration and exploitation balance. Furthermore, we introduce a time-varying inertia weightthat gradually decreases over time, encouraging theparticles to converge towards the optimal solution.To evaluate the performance of our proposed PSO variant, we conduct experiments on a set of benchmark optimization problems. The results demonstrate that our algorithm outperforms traditional PSO and other state-of-the-art optimization techniques in terms of solution quality and convergence speed. Additionally, we demonstrate the effectiveness of our algorithm in solving real-world optimization problems such as function optimization, parameter tuning, and feature selection.In conclusion, PSO is a powerful optimization technique that has found widespread applications in various fields.By incorporating adaptive and time-varying inertia weights, we have developed a novel PSO variant that improves the exploration and exploitation balance, leading to better performance and faster convergence. We believe that this research contributes to the advancement of PSO and its applications in real-world problems.。

螺旋锥齿轮工艺设计论文

螺旋锥齿轮工艺设计论文

, MATLABAbstractSpiral bevel gear, as the machinery industry in the intersecting or staggered axes rotary motion relative to the basis of the important components, with the advantages of overlap coefficient, high carrying capacity, smooth operation, low noise. Therefore it is widely used in various high-speed, heavy mechanical transmission areas. Because of its complex meshing theory, led to processing theory and measurement techniques is also very complex. With the rapid development of computer technology, digital controlKey words:Spiral Bevel Gears Tooth Surface Measuring Data Collection Implementation Program (3) (1)ABSTRACT (2) (4)1.1 (4)1.2 (5)1.3 (13)1.4 (14) (17)3.1 (17)3.2 (19)3.2.1 (20)3.2.2 (20)3.2.3 (20)3.2.4 (21)LGMAZAK (24) (27) (37)6.1 (37)6.1.1 (37)6.1.2 (38)6.1.3 (39)6.1.4 (41)6.2 (42)6.2.1 (42)6.2.2 (44)6.2.3 (46)6.2.4 TURBO (47)6.2.5 (48)6.2.6 (48)6.2.7 (49) (51) (52) (53)1.140(R D)RaDRAD RAD ? ?0 90 1980 76 3 01.2, , , , , ,1.2.1, Olaf . Roemer (1674) , Euler. L ( 1765), , T. Olivier X.N.Foxmah , T. Olivier , , (1820) , , X.N.Foxmah , , T. Olivier , , , , 1820 , , , , 910 , Paul B ttcher, , ( 1.2.4a , single indexing manufact uring met hods) Paul B ttcher , , , , ,1.2.11913 J ames. Gleason 1919 , Paul B ttcher , , , , Ernest Wildhaber ( 1.2.2 a b) No . 16 ,1.2.220 20 , , , , 9 5 - 6 , , , ,, , ,1924 (Shell Oil Company) , , , 1925 , No.16 H 1926 , , , , 1930, , ,1954 , No . 116 ( 1.2.3)1.2.3 No.116( , , , ( 1.2.4)1.2.4 1. 2. 3.4 5. 6.7. 8. 9.10., ( Gleaso n Wo rks) , gelnbergSons) , , , , ,, 1973 , PL C S17 , PL C 1986 , GMA XX 2010 , 1989 , Phoenix CNC Free - form , ( 1.2.5) , , , ,, 1989 , ( KlingelnbergSons) KNC40 , 1.2.51. z2. x3. y4.5. 6. (C )7. (B ) 8. ( A )1.2.5, [6 ] , - ter , , , , ( TCA ) , 50 , , , , F. L . Litvin 60 , , ( 1.2.6) , ,,1.2.6, , , ,1971 , , Gleason , , , Gleason No . 116 , , Gleason , 1999 YK2045 2003 ,1.2.2, , CNC Free - form ——— II ( GEMS ) , II ,, , , , , , , , , GEMS ( GA GE TM 4W I N ,G—A GETM 4W I N , Summary Manager , FEA UM C TM ) , GEMS GEMS : (1) ,GA GETM 4W I N , , , TCA TCA (2) , , , , , , , (3) UM C TM , , , (4) ( FEA) T900 , , (5) , , , , , ( OerlikonBuhrle) ( KlingelnbergSons) , CNC ———KNC/ S35 , , (2001) (2004) , , (2007) , (2006) YK20100 , 11.2.3, , , :1)2 , , , , ,2) , CNC ree-form , , CNC CNC Free - form3) (L TCA) , LTCA4) ,, , , , ,1.31.4(GLEASON) (OERLIKON) (KLINGELNBERG)(GLEASON) (GLEASON)( KLINGELNBERG) HRC58-62NC 80 7% 70% 80 120 200FreeForm——3.1……420CrMnTi20CrMnTi 0.17%-0.24%Cr Mn Ti .20CrMnTi .20CrMnTiGB/T 3077-199910W 1.5kg,3.2—1,3.130.3 0.02 -0.028 -0.03828 0.02 -0.025 -0.03426.3 0.02 -0.025 -0.03425.5 0.02 -0.025 -0.03423.3 0.02 -0.022 -0.03216.96 0.015 -0.018 -0.027 0.1mm1.5kgSW W S ————W ————S>0.63 S>0.32 0.63 S>0.16 0.32 S<0.16M1 M2M1 0.65% 3.0%M2 0.65% 3.0%Cr Mm Ti, 3.0% M1Ra<1.6um3.11.5mm3.23.2.130.3 2.3mm IT12 Ra 12.5um30.3 0.7mm IT9 Ra 1.6um0.08mm Ra 3.2.0.25mm Ra 1.6umR=0.33.2.23.2.31. 150 250HRC58 6498%50HZ 30HZ40-50HZ 24-28A20-306Ra1.614002000070.03 0.05170um1603.2.4253025-50 0.001 0-125 0.01Ra 0.8 1.6 3.2 6.30-1 0.001 0-50030mm 0.021 0.00825mm 0.015 0.0020.015Ra 0.8--- 30 mm 25mm ---105H7100-125Ra 0.8 1.6 3.2 6.3105mm 0.0350.056mm0.03mm0.01mm--- 10500.035 mm ---25mm 0.08-0.15mm0-2.5N·m, 0.05-0.0770 +30% 220-32025mm 0.08-0.18 0.05mm 1.5LGMazaKLGMazaK,QTN-100QTN-150300U 300U 500Uinch 68mm 550mm 185 266 mm 280 330mm 51.mm 109 434 121 431 126 666N(kgf)1470(150)1960 2001960(200)1960 200min35 600035 5000 A2-5A2-6sec 3.3 mm 61 kW(HP) 11(15)7.5(10)15(20)/11(15)N ·m(kgf ·m)161(16.5) MT 4 mm 350325530N(kgf)1962(2000)12820 25mm 32 40sec 0.18/0.430.2\0.5X/Z m/min 30/33 X/Z msec 80/100X190(185+5)Z mm330(325+5)315(310+5)545(540+5) L 130160KVA18.08/23.423.4/31.51020 1670183021301630mm1700 2.72 2.98 3.47kg 3400350037001.2. (3.4.5.6. 7.MAZATROL CNC64 PLCNO NO1X 72Y 83Z Z 94 105 116CNC 12NO NO1 102 113 124 135 146 ( 157 16 (8 179 18,”””ANSI/AGMA.2009-B01 DIN3965-1989 JIS B1704-1973 GB11365-198930%-50%40%-60%10 15mm” , 822H 8620 17CrNiMo6170-210HB01mm0.0051.2.————3.————V/H0.005mm 0.01mm(1)(2)(3)(4)(5)(6) V/H(7)(8)(9)(10)(11)(12)(13)(14)YK20100“””3 ——(1)(2) 1.7m(3) 0.188m(4) 1.888m(5) 20°(6)2ha =s Z Z M21239.046.0 ha1=(7)mn M m s em M R R cos RmReMsm(8)(9)(10)(11)(12)SV=Sinq(13)HcomqS(14)(15)(16) Re6.16.1.14.1.14.1.14.1.2 Fr Fn Fn Fr1. 2. 3. 4. 5.26.1.2V/H/J V/H/J V/H/J V/H/JV/H V/H V/H V/H6.1.3280 320 10KG 4KG0.05MM 20 30 1.6 2L210010Nm0.05MM,6.1.40.08MM 0.08MM6.26.2.1PHOENIX 600HTL CNC TURBOPKOENIX TURBO-LAPPER 600600HTL 3100 TURBO600HTL————V,HCNC600HTL GE FANUC 160CNC 10.4 CNC WINDOWSNT PENTIUN PC CNC PC3.5906.2.2600HTL V, H CNC X ,Y TURBO-LAPPER H 3-2 X ,YEPG600HTL X ,Y V ,H X ,Y Z, V ,HX(H) Z(G)X Y V ,HC, CNC600HTL TURBO-LAPPER CNC6.2.312TURBO600HTL 8000BTU TURMOIL600HTL AC GEFANUC AC GE FANUE6.2.4 TURBO1000 CNC 1800 1800-2200 3000TURBO PHOENIX TURBO 2200/TURBO 3100/12 TURBO6.2.5X ,Y +/-0.00056.2.6600HTL600HTL TURBO0.001.6.2.7X ,Y·· ····。

基于粒子群算法的车身测点布置优化

基于粒子群算法的车身测点布置优化

基于粒子群算法的车身测点布置优化22机械设计与制造MachineryDesign&amp;Manufacture第11期2008年11月文章编号:1001—3997(2008)11-0022—02基于粒子群算法的车身测点布置优化黄伟王华金隼(上海交通大学,上海200240) Placementandoptimizationofmeasuringpointsonbodyinwhitebased onparticleswarmoptimizationHUANGWei,W ANGHua,JINSun (ShanghaiJiaotongUniversity,Shanghai200240,China)此算法具有收敛速度快ementandoptimizationengineeringapplication;Design中图分类号:TH16文献标识码:A上日IJ舌在轿车车身的生产制造过程中,运用三坐标测量机进行测量的目的是通过分析测点的数据,对车身制造过程中产生的制造偏差进行识别与诊断.三坐标测量机测点布置位置的优化很大程度上决定了测量数据的质量,提高了测点对制造偏差的敏感性,因此是车身制造质量控制过程中的重要环节.上海交通大学的周志强等在对检测点进行分类定义后阐述了车身设计阶段各类测点的布置原则,并在此基础上提出了面向制造偏差诊断的测点布置位置优化方法.但是,由于实际生产中车身分总成,总成上的测点一般分布比较多,如果应用此测点布置优化方法,将形成高维空间函数的寻优问题,易于出现收敛速度慢,组合爆炸等问题.所以,将粒子群算法应用于车身测点布置优化中,实例证明此算法具有收敛速度快,解质量高,鲁棒陆好等比,特别适合工程应用.2车身测点布置优化基本方法车身制造过程中主要有零件偏差,定位稳定性,工夹具偏差及零件干涉引起的制造偏差,控制这些偏差是提高车身制造质量的关键,所以在设计车身测点布置方案时应充分考虑测点对制造偏差的敏感性,以此为基本原则来选择测点布置的最佳位置.夹具的误差样本模型可用偏差诊断矩阵D=(d(1),d(2),…,d(n))来描述.D矩阵的第i列是相应于失效类型i的诊断向量(i),i=1,2,…,n,D=dl1d12…d1d2如…dh1d…各诊断向量d(1),d(2),…,d(n)的计算由夹具及测点位置★来稿日期:2008一O1—28的运动几何分析得到.不同的夹具定位方式,工作几何形状,测点位置,决定了不同特性的偏差诊断矩阵D斜体.如果一个具有一个测点设计方案对各种偏差模式(△,AW:,…,AW)都能够很好地区分开来,则这个设计对于制造偏差具有较好的诊断功能,即该测点布置方案具有较强的偏差敏感性.因此将偏差敏感度指数.,定义为:=Vmin∑∑)一)l1(2)i:1J:1式中:d(),d()—第个偏差模式的偏差诊断向量;¨cf()一d(I一两个偏差诊断向量之间的距离范数,偏差敏感度指数-,指的是在所有偏差诊断向量之问相互距离范数中的最小值.偏差敏感度指数.,的实际含义为:测点布置方案具有较大的值,则检测点的布置能够较好地分辨所有的偏差模式.因此,检测点布置的最佳位置就是要找到具有最大偏差敏感度指数的方案,即:=V琦max[minZ∑)一d](3)i=1J=l3选择粒子群算法进行优化操作3.1基本粒子群算法粒子群算法(ParticleSwarmOptimization,PsO)最早是由E—berhart和Kennedy于1995年提出,它的基本概念源于对鸟群觅食行为的研究t3l.在PSO中,每个优化问题的潜在解都可以想象成d维搜索空间上的一个点,我们称之为”粒子”(Particle),所有的粒子都有一个被目标函数决定的适应值(FitnessVMue),每个粒子还有一个速度决定他们飞翔的方向和距离,然后粒子们就追随当前的最优粒子在解空间中搜索.在PSO算法中,若记n为粒子群群体的规模,也就是这个群第11期黄伟等:基于粒子群算法的车身测点布置优化23中粒子的个数,每个粒子的维数为q,则每个粒子表示为:=(,一,‰),每个粒子对应的速度可以表示为:产(,,…,),则粒子群算法的位置速度更新公式为:V~qk+l一_tktck一~2)+cm(pd-*d)(4)=+(5)式中:p—每个粒子自己搜索到的历史最优值,Pi=(pP,…, p),:1,2,…,n,p—全部粒子搜索到的最优值,P=,P醇,…,P),显然每次搜索得出的p只有一个;—保持原来速度的系数,C:—粒子跟踪自己历史最优值和群体最优值的权重系数J,7一[0,1]区间内均匀分布的随机数;r一约束因子.判断该算法的终止条件可以是设置适应值到达一定的数值或者循环一定的次数.3.2车身测点布置优化问题的粒子群算法3.2.1构造粒子群适应度函数如何找到一个合适的表达方法,使粒子与车身测点布置优化问题的解对应,是实现算法的关键问题之一.定义的偏差敏感度指数可表示为:.,=V;min∑∑i=I,=1l=Vmin∑∑i=1i=1d”一d)z+(d一d)…+(d一dx/q)x~,y册尸+...蝴(6)式中:一三坐标测量点的数目;—符号表示测量变量多项式之和.基于式(6),粒子的维数为3,即表示该式中的变量数目,每个粒子可表示为:Xi=(X,Y,…,yz),每个粒子对应的飞行速度可以表示为:(,一,),粒子飞行速度可参照实际的情况来确定.车身测点布置问题的优化方案可描述为:将三坐标的测量变量作为粒子群的粒子,基于实际的工件,给每个粒子赋予一个初始飞行速度,将偏差敏感度指数.,作为适应度函数,通过粒子群位置速度的逐次更新,得到达到最大值的一组粒子,则此粒子对车身制造偏差具有最强的敏感性.3.2_2算法实现过程(1)初始化粒子群,即给群体中的每个粒子赋一个随机的初始值,此初始值可依据三坐标测点布置的经验图纸制定,并给每个粒子赋予一个初始飞行速度;(2)如果达到指定迭代代数,则转步骤(5);(3)根据粒子当前位置,计算其下一个位置,即新解.①由粒子当前速度和位置k及P根据式(4)得出其新的速度vd”;②由粒子当前位置和新的速度根据式(5)得出下一个位置;③由优化函数偏差敏感度指数.,计算出p,如果该解为粒子最优解,则更新p;④如果整个群体找到一个更优的解,则更新P;⑤得出优化结果值.4实例应用以某车型右前侧门外板测点的布置为例来说明粒子群算法在车身测点布置优化问题中的应用.该右前侧门外板由一四位定位销Pl,一二位定位销及c,c,G三个定位块来进行定位,,,帆,眠为四个检测点.该右前侧门外板定位销,定位块及测点的坐标,如表1所示.表1右前侧i”Db板定位销,定位块及测点优化前后坐标值粒子群算法中粒子数目n取为20,粒子的维数为12,初定粒子第一次飞行速度均为10,迭带次数为100次,以偏差敏感度指数t,作为适应度函数,由粒子位置速度更新公式(4),(5)运用Matlab 优化工具,得出最终各粒子最优解P及群体最优解Psa,表略.粒子群的最优解为:p~=[935,722,956,485,812,531,886,734,203,1185,807,509],L:1.852.优化前后右前侧门外板测点的布置方案,如图1所示.图1车身右前侧门外板粒子群算法优化后三坐标测点布置图5结论控制汽车车身制造尺寸偏差是提高车身制造质量的关键因素,随着汽车车身制造技术的不断进步,汽车车身制造的偏差诊断成为车身制造质量控制的难点之一.在对车身测点布置优化的基本方法进行总结改进之后,鉴于应用该方法将形成高维空间函数寻优问题的特点,因此将粒子群算法应用于车身测点布置优化中,实例证明此算法具有收敛速度快,解质量高,鲁棒性好等优点,特别适合此类问题的工程应用.参考文献1YuDing,PansooKim,DariuszCeglarek.OptimalSensorDistributionfor VariationDiagnosisinMultistationAssemblyProcesses.IEEETransactions onRoboticsandAutomation,2003(19)2CaiW,HuSJ,YuanJXDeformableSheetMetalFixturing:Principles,,Al—gorithmsandSimula-tions.JournalofManufacturingScienceand Engineering,1996,118(8)3金隼.基于功能尺寸的车身检测体系优化设计:[硕士学位论文].上海:上海交通大学,2001。

改进混合粒子群算法求解旅行商问题

改进混合粒子群算法求解旅行商问题

改进混合粒子群算法求解旅行商问题
徐福强;邹德旋;章猛;罗鸿赟
【期刊名称】《智能计算机与应用》
【年(卷),期】2022(12)11
【摘要】针对混合粒子群算法在求解旅行商问题(TSP)时容易陷入局部最优导致解的质量下降的情况,提出一种改进的混合粒子群算法。

通过基于贪心策略的粒子初始化操作,使得初始搜索空间质量提高;加入动态学习概率来平衡粒子群算法中的个体学习和群体学习;在个体学习和群体学习中加入Metropolis准则,依一定概率来接受劣解,能够有效地增加种群多样性和提高跳出局部最优的能力;变异操作采用2-opt局部优化的策略,能够有效地解决路径交叉问题,增强算法局部寻优能力,求得更高质量的解。

利用Matlab对改进混合粒子群算法和其他4种算法在TSPLIP实例上进行试验,结果显示改进混合粒子群算法在求解精度、稳定性以及解决较大规模TSP上都具有优势。

【总页数】7页(P229-235)
【作者】徐福强;邹德旋;章猛;罗鸿赟
【作者单位】江苏师范大学电气工程及自动化学院
【正文语种】中文
【中图分类】TP301.6
【相关文献】
1.求解复杂旅行商问题的混合粒子群算法
2.旅行商问题研究及混合粒子群算法求解
3.一种求解旅行商问题的改进混合粒子群算法
4.基于汉明距离的改进粒子群算法求解旅行商问题
5.求解旅行商问题的基于类 Kruskal 的混合粒子群算法
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改进的粒子群优化算法(英文)

改进的粒子群优化算法(英文)

改进的粒子群优化算法(英文)
张雯;杨春明;罗雪春
【期刊名称】《微电子学与计算机》
【年(卷),期】2007(24)2
【摘要】提出了改进的粒子群优化算法。

基于4个不同的基准函数对所提算法与1995年Kennedy和Eberhart提出的常规PSO作了比较。

PSO最初是受到如鸟或鱼等生物群体的社会行为的启发而提出的,每一个体依照自身及群体的过去解决问题的最好办法来调整自己的最佳位置,通过重复这一过程来得出最佳值。

这里提出的改进的PSO的关健之处在于:如果一个新的位置确实得到了改善,则每一个体就调整它的位置;如果不是这样,就根据概率来做出决定。

这一策略是既避免盲目跳转又避免只简单地跳转到好的新位置而陷入局部最优。

模拟结果表明改进的PSO总能比PSO找到更好的解决方法。

【总页数】3页(P70-72)
【关键词】粒子群优化;评估计算;结构最优设计
【作者】张雯;杨春明;罗雪春
【作者单位】辽宁大学;克利夫兰州立大学电子与计算机工程系
【正文语种】中文
【中图分类】TP312
【相关文献】
1.基于粒子群优化算法的神经网络在英文字母识别中的应用 [J], 段其昌;周燕
2.基于改进免疫粒子群优化算法的室内可见光通信三维定位方法 [J], 陈勇;郑瀚;沈奇翔;刘焕淋
3.标准粒子群优化算法的收敛性分析(英文) [J], 傅阳光;周成平;丁明跃
4.基于粒子群优化算法和非线性盲源信号分离测量两相流速度(英文) [J], 吴新杰;崔春阳;胡晟;李志宏;吴成东
5.基于粒子群优化算法的高阶广义屏偏移成像(英文) [J], 何润;尤加春;刘斌;王彦春;邓世广;张丰麒
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基于平滑指数和小波的滚动轴承故障诊断

基于平滑指数和小波的滚动轴承故障诊断

基于平滑指数和小波的滚动轴承故障诊断
赵协广;戴炬
【期刊名称】《轴承》
【年(卷),期】2009(000)011
【摘要】分析了Morlet小波的外形及其适合于分析冲击信号的特点,将平滑指数法引入滚动轴承的故障诊断中,结果表明,平滑指数法明显优于对信号的直接频谱分析,不但检测到了故障特征频率,而且检测到了故障特征频率的倍频,可以简单直观地判别出轴承故障部位.
【总页数】4页(P39-42)
【作者】赵协广;戴炬
【作者单位】山东科技大学机电工程系,山东,泰安,271000;山东科技大学机器人中心,山东,青岛,266510
【正文语种】中文
【中图分类】TH133.33;TH165+.3
【相关文献】
1.基于Hermitian小波的时间-小波能量谱滚动轴承故障诊断方法 [J], 马朝永;王克;孟志鹏;段建民
2.基于双树复小波和AR谱的滚动轴承故障诊断 [J], 宋玉琴;周琪玮;赵攀
3.基于小波包分解和卷积神经网络的滚动轴承故障诊断 [J], 楼剑阳;南国防;宋传冲
4.基于小波包分解和卷积神经网络的滚动轴承故障诊断 [J], 楼剑阳;南国防;宋传冲
5.基于小波包模糊熵与RBF神经网络的滚动轴承故障诊断 [J], 黄芝玲;陈金峰;曾永华;陆筠濠;黎惠敏;朱兴统
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单圈图和双圈图的最大无符号拉普拉斯分离度

单圈图和双圈图的最大无符号拉普拉斯分离度

单圈图和双圈图的最大无符号拉普拉斯分离度
简相国;袁西英;张曼
【期刊名称】《运筹学学报》
【年(卷),期】2015(019)002
【摘要】设G是一个n阶简单图,q1(G)≥q2(G)≥…≥qn(G)是其无符号拉普拉斯特征值.图G的无符号拉普拉斯分离度定义为SQ(G)=q1(G)-q2(G).确定了n阶单圈图和双圈图的最大的无符号拉普拉斯分离度,并分别刻画了相应的极图.
【总页数】6页(P99-104)
【作者】简相国;袁西英;张曼
【作者单位】上海大学理学院数学系,上海200444;上海大学理学院数学系,上海200444;上海大学理学院数学系,上海200444
【正文语种】中文
【中图分类】O157.5;O157.6
【相关文献】
1.单圈图的最大拉普拉斯分离度 [J], 黄冬明;方怡;余桂东
2.双圈图和三圈图的最大拉普拉斯分离度 [J], 余桂东;黄冬明;张午骁;汪宸
3.单圈图的Seidel无符号拉普拉斯能量 [J], 周后卿;徐幼专
4.单圈图的次大(拉普拉斯)分离度 [J], 余桂东;阮征;舒阿秀;于涛
5.三圈图和四圈图的最大无符号拉普拉斯分离度 [J], 剧宏娟;雷英杰
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一种不产生核废物的加速器反应堆

一种不产生核废物的加速器反应堆

一种不产生核废物的加速器反应堆
张果
【期刊名称】《国外核新闻》
【年(卷),期】1995(000)008
【总页数】1页(P10)
【作者】张果
【作者单位】无
【正文语种】中文
【中图分类】TL4
【相关文献】
1.大电感负载平顶平底脉冲大电流的产生:一种新型的同步加速器磁… [J], 张云祥
2.一种不产生候选项集的关联规则挖掘算法 [J], 刘晓玲;李玉忱
3.一种不产生候选项集的关联规则挖掘算法 [J], 李重周;杨君锐
4.一种不产生候选项集的关联规则挖掘算法 [J], 李重周; 杨君锐
5.美国开发熔盐反应堆设想利用核废物产生电力 [J],
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Optimization of spirocyclic proline tryptophan hydroxylase-1inhibitorsDaniel R.Goldberg a ,⇑,Stéphane De Lombaert a ,Robert Aiello a ,Patricia Bourassa a ,Nicole Barucci a ,Qing Zhang a ,Vishwas Paralkar a ,Adam J.Stein b ,Melissa Holt b ,Jim Valentine a ,William Zavadoski aa Karos Pharmaceuticals,401Winchester Ave.,5Science Park,New Haven,CT 06511,United States bCayman Chemical,5025Venture Dr.,Ann Arbor,MI 48108,United Statesa r t i c l e i n f o Article history:Received 7November 2016Revised 19December 2016Accepted 20December 2016Available online 23December 2016Keywords:Tryptophan hydroxylase-1Serotonin TPH-15-HTa b s t r a c tAs a follow-up to the discovery of our spirocyclic proline-based TPH1inhibitor lead,we describe the opti-mization of this scaffold.Through a combination of X-ray co-crystal structure guided design and an in vivo screen,new substitutions in the lipophilic region of the inhibitors were identified.This effort led to new TPH1inhibitors with in vivo efficacy when dosed as their corresponding ethyl ester prodrugs.In particular,15b (KAR5585),the prodrug of the potent TPH1inhibitor 15a (KAR5417),showed robust reduction of intestinal serotonin (5-HT)levels in mice.Furthermore,oral administration of 15b generated high and sustained systemic exposure of the active parent 15a in rats and dogs.KAR5585was selected for further pharmacological evaluation in disease models associated with a dysfunctional peripheral 5-HT system.Ó2016Elsevier Ltd.All rights reserved.Over the years,a growing number of diseases have been associ-ated with a dysfunctional peripheral serotonin system.1These include for example carcinoid syndrome,2gastro-intestinal dis-eases,3–6some fibrotic diseases,7–10obesity associated-fatty liver disease,11atherothrombotic diseases,12–14mastocytosis,15some cancers 16–18and immune diseases,19,20and pulmonary arterial hypertension.21Although it is not always clear if serotonin (5-HT)represents a mere biomarker of these diseases or if it plays an active pathophysiological role,5-HT displays well-known mito-genic and vasoconstrictive properties that may account for its effects.22,23Other mechanisms such as serotonylation processes have also been invoked.24Thus,modulation of peripheral 5-HT tone and 5-HT overproduction in diseased tissues presents a new attractive opportunity to treat these diseases.Intestinal ente-rochromaffin cells located in the gut are responsible for most of the peripheral 5-HT production.5-HT is stored in platelets,with very little amount circulating free,and it does not cross the blood-brain barrier.Therefore,5-HT levels in the periphery and in the CNS are independently regulated.Tryptophan hydroxylase 1(TPH1)is the rate limiting enzyme in the biosynthesis of periph-eral 5-HT,while the centrally located TPH2controls 5-HT produc-tion in the brain.Since reducing central 5-HT levels has been associated with undesirable psychiatric effects,TPH inhibitors,either highly selective for the TPH1isoform or devoid of CNS penetration would be useful to modulate 5-HT peripheral levels.Gut-selective phenylalanine-derived TPH inhibitors have been reported and some of them decrease 5-HT levels in the gut.3,25,26The systemic agent Telotristat ethyl (aka LX1032and telotristat etiprate),a TPH1inhibitor prodrug,showed efficacy in carcinoid syndrome patients unresponsive to somatostatin analog therapy when administered at 250mg three times per day orally during a 12-week period.27Using a combination of a structure-based design and an in vivo efficacy screen,we have discovered several distinct series of TPH1inhibitors.28,29In particular,the spirocyclic proline series provided an attractive starting point to further optimize the inhibitors’efficacy and pharmacokinetic properties,given it already displayed a combination of high in vitro potency and significant mucosal 5-HT reduction in vivo .For example,when dosed orally as its ester prodrug 1b ,the potent TPH1inhibitor 1a elicited a significant 5-HT decrease in gut and platelet rich plasma (PRP)in rats (Scheme 1).28Pharmacokinetic studies showed an 18-fold increase in the plasma exposure (AUC)of 1a after oral administration of 1b vs.1a ,indicating that this prodrug strategy was effective at increasing the systemic delivery of the active inhibitor in rodents.Aiming for an ideal target development candidate profile supportive of a once-a-day regimen,we sought to identify new compounds with more sustained TPH1inhibition compared to 1a .A qualitative metabolite ID study of 1b(d)(diastereomer of 1b )in rat hepatocytes revealed that,in addition to the primary ester hydrolysis pathway,the methyl group of the pyrazole substituent/10.1016/j.bmcl.2016.12.0530960-894X/Ó2016Elsevier Ltd.All rights reserved.⇑Corresponding author.E-mail address:dgoldberg@ (D.R.Goldberg).Bioorganic &Medicinal Chemistry Letters 27(2017)413–419Contents lists available at ScienceDirectBioorganic &Medicinal Chemistry Lettersjournal homepage:www.else v i e r.c o m /l o c a t e /b m clTable1Enzymatic activity of biarylderivatives.Cpd R TPH1IC50(nM)Cpd R TPH1IC50(nM) 2a Cl4607a323a258a32 4a269a37414 D.R.Goldberg et al./Bioorganic&Medicinal Chemistry Letters27(2017)413–419underwent further hydroxylation.Further processing led to the carboxylic acid and sulfate metabolites (Scheme 2).Although these metabolites were not quantified,this study sug-gested that modifications of the pyrazole group could lead to phar-macokinetic improvements in rats,and perhaps ultimately in humans.The known plasticity of the hydrophobic pocket of TPH1apparent in published X-ray structures supported the notion that a broad structural diversity in the corresponding lipophilic region of the inhibitor should be tolerated to maintain high potency.30Since complete removal of the pyrazole group resulted in a sig-nificant loss of potency,a focused library was produced to identify aryl alternatives to the 4-chloro substituent able to restore potency.Within this set,a small number of bicyclic aromatics led to compounds with a level of potency in the range of 1a (Table 1).Despite their intrinsic potency,these compounds failed to demonstrate robust 5-HT reduction in vivo when administered orally at 50mg/kg to mice,31either by themselves or as their cor-responding ethyl ester prodrugs (data not shown).Potency and efficacy of some biaryl derivatives could be restored once the pyra-zole group was re-introduced (Table 2).Since the prodrug 13b displayed a robust reduction of mucosal 5-HT in mice after oral administration,its PK in rats was deter-mined (Table 3).Interestingly,despite the observed in vivo efficacy,the overall oral exposure of the active inhibitor was low,raising the possibility that active metabolites may be generated.In particular,the two mono-and the di-hydroxymethyl derivatives of 13a were determined to be potent TPH1inhibitors enzymatically too (data not shown).Therefore,we focused on modifying the pyrazole directly to overcome the potential metabolic issues associated with this group.Replacements of this heterocycle with a simple phenyl group provided a potent TPH1inhibitor (15a ).A high resolution crystal structure (1.9–2.0Å)of 15a bound to TPH1was obtained (pdb code:5L01),revealing key interactions (Fig.1).The proline NH forms a hydrogen bond with T265,while the carboxylate engages R257.The chloro phenyl group p -stacks with the flexible Y235residue.While similar interactions had also been observedTable 2Enzymatic and in vivo activity of compounds with aryl alternatives to chloro substituent in 1a .Cpd R TPH1IC 50(nM)aEthyl ester prodrug%5-HT Reduction (50mg/kg PO)b1a261b 6112a2212b6013a 2013b 7014a2314b 46a IC 50values reported as the mean of at least two determinations.bData expressed as mean changes relative to vehicle control.All changes relative to vehicle significant by 1-Way ANOVA (P <0.001).Table 1(continued )aaaIC 50values reported as the mean of at least two determinations.Table 3Plasma rat PK for 13a measured after 13b po dosing.PK parameter a Result iv t 1/2(h) 3.6±0.71po C max (ng/mL)240±60.9po AUC 0-last (h*ng/mL)1634±321aProtocols:25mg/kg po in 0.5%MC in water;1mg/kg iv in 1%DMSO/5%Solutol HS15/94%saline;all values expressed as mean ±SD.D.R.Goldberg et al./Bioorganic &Medicinal Chemistry Letters 27(2017)413–419415Fig.1.(A)Binding conformation of15a(brown,grey)in TPH1(pdb code5L01).Dotted lines depict H-bond interactions between the inhibitor and TPH1residues R257 sidechain and T265backbone(clearly resolved).The orange sphere is the Fe+2atom.(B)Overlay between15a(brown,grey)and the phenyl alanine based TPH1inhibitor LP-533401(pdb code:3HF8)(magenta).30Table4Enzymatic activity of compounds with aryl and heteroaryl alternatives to pyrazole in1a.a a aCl18aO1926aClF3534aN73 19aFFF2127aFF3535aNO77416 D.R.Goldberg et al./Bioorganic&Medicinal Chemistry Letters27(2017)413–419with the known phenylalanine-derived TPH1inhibitors,30R257undergoes a conformational change to accommodate the proline moiety while maintaining the conformation of S265and S337.As anticipated,the three dimensional spirocyclic proline uniquely fills the amino acid binding pocket more fully than the phenylalanine-based chemotypes.The new phenyl substituent is not pointing outwards to the solvent,but resides in a wide but rather short lipo-philic pocket of TPH1capped by a hydrophobic triad consisting of Y312,F241and L242.This binding conformation suggested that further substitution in the meta position of the R-group phenyl ring would be tolerated,while only small groups would be allowed in the para position.Therefore,a focused library was prepared to identify tolerated substituted aryl or heteroaryl alternatives to the pyrazole (Table 4).Table 4(continued )aaaaIC 50values reported as the mean of at least two determinations.parison between 17a (cyan)and 15a (grey,brown).(A)Y235re-orientation allows better p interaction with the chloro phenyl ring.(B)One oxygen of the sulfonyl group engages in a hydrogen bond with a conserved water molecule.Table 5Mucosal 5-HT reduction after oral dosing of ethyl ester prodrugs of potent TPH1inhibitors in mice.Ethyl ester prodrugdosed poActive parent generated%5-HT reduction a(50mg/kg)(10mg/kg)15b 15a 71⁄⁄⁄55⁄⁄⁄16b 16a 42⁄⁄15⁄17b 17a 38⁄⁄ND b 18b 18a 46⁄⁄⁄19⁄19b 19a 64⁄⁄⁄31⁄⁄20b 20a 37⁄⁄30⁄⁄21b 21a 59⁄⁄⁄33⁄⁄22b 22a 54⁄⁄⁄24⁄⁄25b25a61⁄⁄⁄38⁄⁄aData expressed as mean changes relative to vehicle control.All changes relative to vehicle significant by 1-Way ANOVA:P <0.001⁄⁄⁄,P <0.01⁄⁄,P <0.1⁄.bNot determined.D.R.Goldberg et al./Bioorganic &Medicinal Chemistry Letters 27(2017)413–419417Phenyl groups substituted in the meta position with a wide variety of functional groups,provided potent TPH1inhibitors. Large alkyl groups,such as tBu(21a)were well tolerated.Methyl sulfone(17a)and unsubstituted sulfonamides(16a)were particu-larly active,in particular when compared to carboxamide(28a)or reversed-sulfonamide analogs(29a).Even a larger pyrrolidinone substituent retained some potency(35a),confirming the rather wide space available in the corresponding enzymatic pocket. Substitution of the sulfonamide nitrogen led to a decrease in potency(31a).As predicted,para-substitution,in particular with large groups,resulted in a loss of activity.Basic pyridine groups (33a,34a)were less favorable than aryl groups for activity.A high resolution X-ray crystal structure of17a(KAR5489,1.50–1.55Å)bound to TPH1(pdb code:5TPG),confirmed the expected binding conformation(Fig.2).While15a and17a adopt a similar binding pose in the TPH1active site,the side-chain of Y235adopts a slightly different orientation.In17a,Y235rotates closer to the chloro phenyl ring to engage in a stronger p–p inter-action with the ligand’s aromatic ring.As the sulfonyl group sits further into the lipophilic pocket,a water-mediated hydrogen bond is also observed.Given that a large number of compounds were potent TPH1 inhibitors in vitro,we next sought to assess their effects in vivo.A set of potent inhibitors was evaluated orally as their corresponding ethyl esters in mice,at doses of50and10mg/kg and mucosal5-HT levels were determined(Table5).While most orally administered prodrugs robustly decreased intestinal5-HT concentrations at50mg/kg,their efficacy dropped off significantly at the lower10mg/kg dose.Interestingly,the unsubstituted analog15b,remained efficacious at the low dose. Therefore,the PK of15b was elucidated in rat,a species used in various pharmacodynamics models(Table6).Compared to1b,15b generated the active parent15a with a lower C max and larger AUC,a PK profile more attractive to achieve sustained inhibition with less peak-to-trough variations.Although15a(and the other spirocyclic proline analogs)also showed potent inhibition of TPH2(IC50=7nM),an enzyme responsible for5-HT production in the CNS,15a and15b were found to be essentially confined to peripheral tissues in rats (data not shown).To verify that effective bioconversion of the prodrug also occurred in higher species,15b was administered orally to beagle dogs(Table7).High exposure of15a was achievable in dogs after administra-tion of the ethyl ester prodrug15b.The synthesis of15b and15a is representative of the method used to prepare the compounds described herein(Scheme3).In brief,(R)-1-(2-bromo-4-chlorophenyl)-2,2,2-trifluoroethanol(16) was added to2-amino-4,6-dichloropyrimidine to provide interme-diate17,to which the suitable doubly protected(S)-spirocyclic proline ester reagent was added.32Regioselective Pd-catalyzed arylation of18with phenylboronic acid afforded the biaryl inter-mediate19.To obtain15b,the Cbz protecting group was removed from19via in situ production of TMSI,after having found that deprotection by hydrogenation led to the formation of an unsepa-rable by-product lacking the chloro substituent.Final chemical saponification afforded the active TPH1inhibitor15a.This syn-thetic route provided15a and15b in good yield and high chemical and optical purities(>99%ee).In summary,we have described herein an extension of the SAR of our spirocyclic proline-based TPH1inhibitors.15In particular,we have identified15a(KAR5417),a compound when masked as its corresponding ethyl ester prodrug15b(KAR5585),produces robust5-HT reduction in mice intestinal mucosa.Oral administra-tion of KAR5585generated substantial and sustained systemic exposure of the parent TPH1inhibitor in rat and dog.Therefore, this compound was advanced in disease-relevant models of dysfunctional peripheral5-HT.This work and the resulting therapeutic potential of KAR5585will be reported in subsequent publications.Table6Plasma rat PK for15a and15b(iv and po),and comparison with1a generated from1b po dosing.Compound dosed15b iv b15b po c15b po c15a iv d1b po c Compound measured a15b15b15a15a1a PK ParameterCl(ml/min/kg)[%hepatic bloodflow]106.2±22.1[>100]0.901±0.07[1.2]–iv t1/2(h) 3.18±0.2712.1±1.7–iv Vss(L/kg)12.7±2.5–7.8±1.33–po C max(ng/mL)111±21.8722±93.0–2,253±47 po AUC0-last(h*ng/mL)809±1365,374±866–2,924±600a All values expressed as mean±SD.b Protocol for15b iv dosing:1mg/kg in1%DMSO/5%Solutol HS15/94%saline.c Protocol for15b and1b po dosing:25mg/kg in0.5%MC in water,8time points(0.083–24h).d Protocol for15a iv dosing:1mg/kg in2.5%DMSO/5%solutol HS15/92.5%saline.Table7Plasma dog PK for15a and15b(iv and po).Compound dosed15b iv b15b po c15b po c15a iv d Compound measured a15b15b15a15a PK parameterCl(ml/min/kg)[%hepatic bloodflow]0.83±0.15[46]–– 1.2±0.01[4%] iv t1/2(h) 5.05±0.347.59±0.28 4.8±0.76iv Vss(L/kg) 2.34+0.29–0.24±0.03 po C max(ng/mL)744±2182,797±713–po AUC0-last(h*ng/mL)4,564±89932,398±11,975–a All values expressed as mean±SD.b Protocol for15b iv dosing:1mg/kg in1%DMSO/5%Solutol HS15/94%saline.c Protocol for15b po dosing:25mg/kg in0.5%MC in water,8time points(0.083–24h).d Protocol for15a iv dosing:1mg/kg in2.5%DMSO/5%solutol HS15/92.5%saline.418 D.R.Goldberg et al./Bioorganic&Medicinal Chemistry Letters27(2017)413–419AcknowledgementsThe authors thank Scynexis and Santai Labs for their assistance in preparing synthetic intermediates and/orfinal compounds and ChemPartner for their help with DMPK work in this paper. 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