COMPUTER CONTROL SYSTEM FOR DRAWING MACHINE IN HORIZONTAL CONTINUOUS CAST SET

A computer control system for drawing machine in horizontal continuous cast set was introduced.The operation features of the drawing machine were analyzed»the hardware configuration and principles of interface circuit for stroke measurements were given out.An effective method was provided,which made the process parameters progressively optimize under the software environment using friendly interface of person-and-computer communication.This method was also adaptable to optimize parameters of other production process which are hard to model.

Finite-size analysis of eight-state continuous-variable quantum key distribution with the linear optics cloning machine

We propose a method to improve the secret key rate of an eight-state continuous-variable quantum key distribution（CVQKD） by using a linear optics cloning machine（LOCM）. In the proposed scheme, an LOCM is exploited to compensate for the imperfections of Bob＇s apparatus, so that the generated secret key rate of the eight-state protocol could be well enhanced. We investigate the security of our proposed protocol in a finite-size scenario so as to further approach the practical value of a secret key rate. Numeric simulation shows that the LOCM with reasonable tuning gain λ and transmittance τcan effectively improve the secret key rate of eight-state CVQKD in both an asymptotic limit and a finite-size regime.Furthermore, we obtain the tightest bound of the secure distance by taking the finite-size effect into account, which is more practical than that obtained in the asymptotic limit.

An Optimal Feed Interpolator Based on G^2 Continuous Bézier Curves for High-Speed Machining of Linear Tool Path

A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.

Pre-Compensation for Continuous-Path Running Trajectory Error in High-Speed Machining of Parts with Varied Curvature Features

Parts with varied curvature features play increasingly critical roles in engineering, and are often machined under high-speed continuous-path running mode to ensure the machining efficiency. However, the continuous-path running trajectory error is significant during high-feed-speed machining, which seriously restricts the machining precision for such parts with varied curvature features. In order to reduce the continuous-path running trajectory error without sacrificing the machining efficiency, a pre-compensation method for the trajectory error is proposed. Based on the formation mechanism of the continuous-path running trajectory error analyzed, this error is estimated in advance by approximating the desired toolpath with spline curves. Then, an iterative error pre-compensation method is presented. By machining with the regenerated toolpath after pre-compensation instead of the uncompensated toolpath, the continuous-path running trajectory error can be effectively decreased without the reduction of the feed speed. To demonstrate the feasibility of the proposed pre-compensation method, a heart curve toolpath that possesses varied curvature features is employed. Experimental results indicate that compared with the uncompensated processing trajectory, the maximum and average machining errors for the pre-compensated processing trajectory are reduced by 67.19% and 82.30%, respectively. An easy to implement solution for high efficiency and high precision machining of the parts with varied curvature features is provided.

American Factories Continue to Increase Equipment Investment——Competitors in Asia also increased their consumption of machine tools.

去年美国金属加工工厂对新机床产品的需求量较 2004 年和 2003 年都有增加。消沉中的美国资本投资发生了一个转变,排在德国之前成为第三大消费国。然而对于美国机床业来说,不太好的消息是亚洲竞争对手同样增加了他们在机床的设备投资。来自第 41 次世界机床生产和消费的调查显示,中国、日本、韩国、印度以及中国台湾地区的机床消费都有显著增长。在 28 个国家和地区的调查中,四分之三的国家和地区消费都在增长,这同时也诠释了经济“表面消费”的含义。这一切表明美国的机床消费量还会继续保持增加,订购机器的数量会维持向上的势头,最新的数据也证明了这一点。此外,调查数据还罗列了其它国家,尤其是亚洲国家的机床生产情况(本刊2006 年第一期环球瞭望栏目文章图表中显示了世界主要机床生产状况):德国和日本领导着世界机床的生产;中国超过意大利成为第三大机床生产国;中国台湾地区排在第五位;美国机床的生产和出口排在台湾之后,保持小的增长;韩国与瑞士紧追其后;印度也毫不示弱。这28个被调查国家和地区的机床产量都比前一年平均增长了14% 。中国、日本、韩国、印度以及中国台湾地区占据了整个调查地区机床产量的 47.6% ,作为CECIMO 成员的由 15 个国家组成的西欧联盟占据了机床产量的 4 2 . 3 % 。

Study on Control Algorithm for Continuous Segments Trajectory Interpolation

In CNC machining,the complexity of the part contour causes a series of problems including the repeated start-stop of the motor,low machining efficiency,and poor machining quality.To relieve those problems,a new interpolation algorithm was put forward to realize the interpolation control of continuous sections trajectory.The relevant error analysis of the algorithm was also studied.The feasibility of the algorithm was proved by machining experiment using a laser machine to carve the interpola- tion trajectory in the CNC system GT100.This algorithm effectively improved the machining efficiency and the contour quality.

Neutron-gamma discrimination method based on blind source separation and machine learning

The discrimination of neutrons from gamma rays in a mixed radiation field is crucial in neutron detection tasks.Several approaches have been proposed to enhance the performance and accuracy of neutron-gamma discrimination.However,their performances are often associated with certain factors,such as experimental requirements and resulting mixed signals.The main purpose of this study is to achieve fast and accurate neutron-gamma discrimination without a priori information on the signal to be analyzed,as well as the experimental setup.Here,a novel method is proposed based on two concepts.The first method exploits the power of nonnegative tensor factorization(NTF)as a blind source separation method to extract the original components from the mixture signals recorded at the output of the stilbene scintillator detector.The second one is based on the principles of support vector machine(SVM)to identify and discriminate these components.In addition to these two main methods,we adopted the Mexican-hat function as a continuous wavelet transform to characterize the components extracted using the NTF model.The resulting scalograms are processed as colored images,which are segmented into two distinct classes using the Otsu thresholding method to extract the features of interest of the neutrons and gamma-ray components from the background noise.We subsequently used principal component analysis to select the most significant of these features wich are used in the training and testing datasets for SVM.Bias-variance analysis is used to optimize the SVM model by finding the optimal level of model complexity with the highest possible generalization performance.In this framework,the obtained results have verified a suitable bias–variance trade-off value.We achieved an operational SVM prediction model for neutron-gamma classification with a high true-positive rate.The accuracy and performance of the SVM based on the NTF was evaluated and validated by comparing it to the charge comparison method via figure of merit.The results indicate that the proposed approach has a superior discrimination quality(figure of merit of 2.20).

An Algorithm for Idle-State Detection and Continuous Classifier Design in Motor-Imagery-Based BCI

Abstract-The development of asynchronous brain-computer interface （BCI） based on motor imagery （M1） poses the research in algorithms for detecting the nontask states （i.e., idle state） and the design of continuous classifiers that classify continuously incoming electroencephalogram （EEG） samples. An algorithm is proposed in this paper which integrates two two-class classifiers to detect idle state and utilizes a sliding window to achieve continuous outputs. The common spatial pattern （CSP） algorithm is used to extract features of EEG signals and the linear support vector machine （SVM） is utilized to serve as classifier. The algorithm is applied on dataset IVb of BCI competition Ⅲ, with a resulting mean square error of 0.66. The result indicates that the proposed algorithm is feasible in the first step of the development of asynchronous systems.

Continuity control method of cutter posture vector for efficient five-axis machining

During five-axis machining of impeller, the excessive local interference avoidance leads to inconsistency of cutter posture, low quality of machined surface and increase of processing time. Therefore, in order to improve the efficiency of five-axis machining of impellers, it is necessary to minimize the cutter posture changes and create a continuous tool path while avoiding interference. By using an MC-space algorithm for interference avoidance, an MB-spline algorithm for continuous control was intended to create a five-axis machining tool path with excellent surface quality and economic feasibility. A five-axis cutting experiment was performed to verify the effectiveness of the continuity control. The result shows that the surface shape with continuous method is greatly improved, and the surface roughness is generally favorable. Consequently, the effectiveness of the suggested method is verified by identifying the improvement of efficiency of five-axis machining of an impeller in aspects of surface quality and machining time.

Continuous Variable Quantum MNIST Classifiers—Classical-Quantum Hybrid Quantum Neural Networks

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to nm classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size nm. They are then interpreted as one-hot encoded labels, padded with nm - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.

考虑碾压参数影响的压实质量连续检测结果修正研究

为降低压路机行驶速度、行驶方向、振动频率等碾压参数引起的压实质量连续检测结果CMV误差,以动态变形模量E_(vd)为修正目标,基于极限梯度提升XGBoost算法和多元线性回归方法分别建立CMV修正模型,对比修正模型的适用性,并分析各碾压参数对CMV误差的影响。研究表明,XGBoost修正结果与E_(vd)的相关系数高达0.8且误差仅为1.7%,而多元线性回归修正结果与E_(vd)的相关系数仅为0.65且误差高达9.2%,XGBoost修正模型更适用于降低由碾压参数引起的CMV检测误差。高速行驶会造成CMV存在显著的负误差,行驶方向的不同使CMV产生大小相当但正负相反的误差,振动频率对CMV误差的影响是非线性的且与频率的取值有关。最后根据现场压实检测数据的修正结果验证了XGBoost修正模型对降低CMV检测误差的可靠性。

人机一体、虚拟影像与伦理预警——人工智能时代电影工业美学“接着讲”之一

以人工智能为引擎的高新科技加速度式发展,给电影工业,电影人的思维、想象力、主体性与工作理性、生存方式等都带来巨大冲击,也对电影理论建设提出了新挑战。电影工业美学立足于电影产业发展、工业化升级和新生代导演的创作,涉及原理论、方法论、创作论、本体论、受众论、传播论等维度,系新媒介语境下建构的电影阐释学术话语和知识体系。当下的电影工业美学需要在人工智能背景下“接着讲”:在主体维度,“人机一体”成为导演身体的延伸、器官的再造,带来了主体性危机;在影像维度,大量智能化生成的虚拟影像颠覆了影像本体论与真假标准;在伦理维度,必须关注日益加剧的内部/外部电影伦理问题,遵循“以人为本”原则。

可实现连续分度加工的摆线圆柱齿轮研究

研究了一种可采用连续分度铣削法进行加工的摆线圆柱齿轮。与传统的圆柱齿轮不同,这种齿轮的轮齿沿其轴线方向呈摆线形。该齿轮具有承载能力大、传动平稳、无轴向力等特点;尤其是可采用连续铣削的加工方法,大大提高加工效率。摆线圆柱齿轮可采用圆盘铣刀加工,圆盘铣刀上装有可更换的刀片,同一模数的齿轮只需要一把盘铣刀,通过更换刀片可进行不同模数齿轮的加工。首先,基于齿轮啮合理论推导了摆线圆柱齿轮的数学模型,利用自行开发的计算软件生成离散齿面点,建立三维模型;然后,阐述了圆盘铣刀的几何设计和齿轮的加工原理;最后,从齿轮特性、加工效率和标准化生产3个方面说明了该齿轮的优势。研究为圆柱齿轮的设计和加工提供了新的思路和基础。

基于人机连续问答的疾病流调与随访智能技术研究

目的 解决流调和随访信息收集过程中依赖大量人力、问答模板单一、反复多次、效率低等问题。方法研究面向流调随访信息自动收集的人机连续问答技术,实现流调随访问答模板的自动生成和流调随访信息可信度评估,形成高效、可信、鲁棒的疾病流调和随访信息自动收集方案。结果 通过基于人机连续问答的疾病流调与随访智能技术研究,并开展规模化验证与示范应用,显著提升流调随访效率和精准度。结论 通过构建与新技术有效配合的精准流调随访业务模式,为形成和推广面向现代疾病防控的智能化信息采集与解析解决方案提供较为成熟、可复制的经验模式。

一种锁芯片零件加工连续级进冲压模具的设计分析

为了实现锁芯片零件的全自动化加工,针对一种锁芯片零件,设计了一种连续级进冲压加工模具,解决了该零件打凹、冲孔及落件多道工序的加工问题,并且大大提高了该零件生产的自动化程度、生产效率及加工精度。

基于异构集成模型的连续刚构桥预拱度预测

预拱度在大跨度悬臂桥梁的施工线形监控中扮演着重要角色,提高预测精度能够确保施工阶段和成桥状态的线形尽可能符合设计要求。为获得更好的预测性能,本文提出了一种基于自适应集合加权的SSA-BPNN-RF(sparrow search algorithm-back propagation neural network-random forest)异构集成模型。该模型利用不同算法之间的协作来提高预测性能,为了验证该模型的可行性,将训练好的模型应用于湖南某连续刚构桥预拱度预测,并与BPNN、RF、BPNN-RF、SSA-BPNN和SSA-RF 5种预测模型进行对比。研究结果表明:SSA-BPNN-RF异构集成模型在平均绝对误差、均方根误差和拟合度等评价指标上表现最佳。此外,BPNN-RF集成和SSA分别对BPNN和RF都有积极的影响,进一步验证了异构集成的有效性。因此,SSA-BPNN-RF异构集成模型具有高精度和更好的适应性,在工程实践中具有重要的指导意义。

连铸机末端重压下对高建Z向钢铸坯质量的影响

针对铸坯内部中心偏析、板材探伤合格率(80%以下)低的问题,河钢集团唐山中厚板材有限公司在280 mm×1800 mm断面板坯连铸机10#、11#扇形段位置实施了重压下技术,总压下量达到24 mm,在生产高建Z向钢时,铸坯致密度提高,中心偏析明显改善,铸坯晶粒尺寸随着压下量的提高显著细化。利用低倍、金相组织分析,以及铸坯碳偏析指数、致密度和真密度等实验,分析了重压下对连铸坯内部质量的影响。结果表明:随着压下量的增加,铸坯中心偏析与疏松逐步改善,低倍C级品率由原来的85%提高至98%;连铸坯断面组织也发生晶粒的细化,有明显的细化晶粒效果;当铸坯中心偏析为C级1.0时,中心碳偏析指数均可有效控制在1.10以内;铸坯的真密度整体提升,重压下工艺下铸坯中心位置真密度提升0.17%;C级≤0.5级比例≥98%;铸坯中心偏析C级率(≤0.5级比例)综合比例达92.9%。重压下技术应用后,高建Z向钢铸坯晶粒尺寸随着压下量的提高显著细化,中心偏析得到明显改善。

Technical innovation in the “Beishan No. 1” hard rock tunnel boring machine for high-gradient spiral tunnels

1 Project overview The“Beishan No.1”,the world’s first-ever hard rock Tunnel Boring Machine(TBM)tailored for high-gradient spiral tunnels,constitutes a pivotal element within the Beishan Underground Research Laboratory(URL)initiative in China.Beishan URL,the first URL for geological disposal of high level radioactive waste(HLW)in China,is a national key construction project listed in the“13th Five-Year Plan”.In 2019,subsequent to receiving approval from the China Atomic Energy Authority,the Beijing Research Institute of Uranium Geology(BRIUG),serving as the project’s owner,initiated its construction.This underground facility,categorized as a“third generation”URL for HLW disposal,i.e.,area-specific URL,was located in Beishan,Jiuquan City,Gansu Province,China,following more than three decades of rigorous research on site selection.

基于连续小波变换和机器学习的小麦产量预测

及时、准确的作物估产,对作物管理决策和粮食安全评估具有重要意义。该研究在建立一种耦合连续小波变换(CWT)与机器学习准确预测小麦产量的方法。基于2020年—2021年两年小麦田间试验获取的开花期和灌浆期冠层高光谱数据及产量数据,首先采用CWT提取三种小波特征(WFs),分别为:基于Bortua方法筛选的特征波段(Bortua-WFs)、提取WFs与小麦产量确定系数的前1%(1%R^(2)-WFs)和单一分解尺度下的所有WFs(SS-WFs)。然后采用随机森林(RF)、 K最邻近(KNN)和极端梯度提升(XGBoost)三种机器学习算法构建产量预测模型。最后选取最优的光谱特征,采用相同的方法进行建模并比较。结果表明:(1)三种WFs结合机器学习方法的模型均表现良好,基于Bortua-WFs构建的模型具有更高的精度和稳定性。(2)相比光谱特征模型,Bortua-WFs模型在各生育期的精度均有所提高,开花期的R^(2)精度分别提高了17.5%、 4%和39.6%,灌浆期分别提高了8.4%、 5.6%和16.9%。(3)灌浆期的产量估算模型优于开花期,结合Bortua-WFs和XGBoost的模型表现最佳,R^(2)为0.83, RMSE为0.78 t·ha^(-1)。该研究比较了不同特征和方法相结合的性能,确定了不同方案下的最佳模型精度,为光谱准确预测小麦产量提供技术参考。

2024中国国际纺织机械展览会暨ITMA亚洲展览会针织物印染前处理机械述评

介绍展览会展出的针织物烧毛机、棉及其混纺或交织针织物连续化练漂机、棉针织物丝光机、化纤针织物连续化精练机和适于针织物的松式连续化水洗机等设备,包括设备外观结构图、工艺流程图和应用技术特点等,并进行述评,指出:针织物开幅平幅连续化练漂机、除油精练机和水洗机的制造及应用技术虽已趋于成熟,但实际应用的厂家很少;绳状松式运行的连续化练漂机、水洗机呈现快速发展的态势,其适应织物范围广和应用工艺范围广以及松式运行方式等,可弥补开幅平幅练漂机和水洗机的不足;圆筒状针织物无须剖幅就能松式运行的连续化练漂机有许多突出优点;烧毛、丝光保证了棉型针织物布面光洁度和平整度以及尺寸稳定性,应成为针织物染整技术的重要工序。