A regression approach for seismic first-break picking

The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.

Enhancing manual P-phase arrival detection and automatic onset time picking in a noisy microseismic data in underground mines

Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.

Automatic onset phase picking for portable seismic array observation

Automatic phase picking is a critical procedure for seismic data processing, especially for a huge amount of seismic data recorded by a large-scale portable seismic array. In this study is presented a new method used for automatic accurate onset phase picking based on the proporty of dense seismic array observations. In our method, the Akaike＇s information criterion （AIC） for the single channel observation and the least-squares cross-correlation for the multi-channel observation are combined together. The tests by the seismic array observation data after triggering with the short-term average/long-term average （STA/LTA） technique show that the phase picking error is less than 0.3 s for local events by using the single channel AIC algorithm. In terms of multi-channel least-squares cross-correlation technique, the clear teleseismic P onset can be detected reliably. Even for the teleseismic records with high noise level, our algorithm is also able to effectually avoid manual misdetections.

SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces

Manually picking regularly and densely distributed first breaks(FBs)are critical for shallow velocitymodel building in seismic data processing.However,it is time consuming.We employ the fullyconvolutional Seg Net to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces.Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs,we can obtain a welltrained Seg Net model.When any unseen gather including the one with irregular trace spacing is inputted,the Seg Net can output the probability distribution of different categories for waveform classification.Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background.Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer Seg Net can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones,even when the proportion of randomly missing traces reaches50%,21 traces are missing consecutively,or traces are missing regularly.

Accurate and rapid image segmentation method for bayberry automatic picking via machine learning

Due to the short ripening period and complex picking environment,bayberry generally relies on mechanical equipment for picking,especially the automatic picking system guided by vision.Thus,it is crucial to locate the bayberry in the view accurately and rapidly.Although efforts have been made,the existing methods are difficult to implement due to the limited amount of data and the processing speed.In this study,an accurate and rapid segmentation method based on machine learning was proposed to address this problem.First,the images collected by the visual guidance system were pre-processed by contrast-limited adaptive histogram equalization(CLAHE)based on the Y component of the YUV color space.Taking advantage of the color difference map of RB and RG for the segmentation of different colors,an adaptive color difference map foreground segmentation method was then adopted for bayberry region foreground segmentation.Finally,distance transforms and marking control watershed methods were exploited to achieve single bayberry fruit segmentation.Furthermore,with the help of the convex hull theory and fruit shape characteristics,the irregular background interference areas were filtered out,which improved the accuracy of bayberry segmentation performance.The experimental results show that this method can achieve better segmentation of bayberry in complex orchard environment with an accuracy of 97.4%and only takes 0.136 s to calculate once.

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

烟草钵苗双盘对称交错式自动取苗装置设计与试验

为解决烟草半移栽作业用工量大、效益低等问题,设计一种对称交错式自动取苗装置。首先,阐述该自动取苗装置对两盘钵苗进行交错式取苗的原理,建立取投苗作业过程中的动力学和运动学模型。然后,对送盘装置、取投苗装置和自动控制系统等取苗装置关键部件进行结构设计,分析关键部件的作业过程,结合实际需求完成关键元器件的选型。最后,在已有移栽机动力底盘的基础上试制2ZY-1型烟草移栽机样机,以60天苗龄的烟草钵苗为作业对象进行田间取投苗性能试验。试验结果表明,在自动取投苗速度为20株/min时,取苗成功率为89.63%,投苗成功率为97.20%,伤苗率为3.59%。

智慧采摘机器人

随着现代农业智能化技术的飞速发展,智慧农业系统的应用越来越多。农业采摘机器人作为智慧农业的一个新兴领域,成为近年来研究的热点。本文利用STM32微控制器,设计出一款适用于果园的自动采摘机器人,实现水果自动化采摘,用以解决农业领域中人力短缺和低效率的问题。该机器人具有成本低廉,高效精准的特点,具有提高采摘效率和质量,降低人工采摘成本的优点,适合推广应用。

基于机器视觉的苹果自动采摘分拣系统研究

为提升苹果自动化采摘、分拣效率,设计了一种基于机器视觉的苹果自动采摘分拣系统。为设计该系统,首先基于对采摘机器人机械手和末端执行器结构、机械手避障的分析,提出采摘机器人的苹果采摘流程;然后,搭建了基于实时数据处理的苹果自动分拣平台,该平台由两类传送带、可编程逻辑控制器、个人计算机和带有机器视觉、称重传感器和控制面板单元的封闭舱等组成。为验证所构建苹果自动分拣系统的有效性,对来自3个不同苹果品种的183个苹果进行了试验。试验结果表明,所构建的苹果自动采摘分拣系统对苹果按照颜色、尺寸分拣的准确率分别达到96.17%和92.77%,按重量分拣时对苹果重量的估算误差只有5.44克,同时还可对结痂、污点、腐烂等缺陷区域进行100%准确检测。所设计的苹果自动采摘分拣系统具备较高采摘分拣效率,从而对促进苹果产业的发展具有一定实用价值。

基于谱残差法的主动声呐疑似目标自动拾取

针对主动声呐干扰遍布的B显画面上目标亮点难以拾取的问题,本文提出一种基于谱残差法视觉显著性处理的疑似目标自动拾取方法。在主动声呐图像的概念之下,研讨了自然图像中视觉显著体的发现与主动声呐图像中疑似目标拾取的相似机理,依此进而说明了本文方法的机理、核心框架以及关键处理环节,包括预处理声呐图像、生成显著图、估算拾取阈值以及标定疑似目标等。海试数据处理结果表明:相较于基于局部相对能量分析的对照方法,本文方法对已知目标具有更低的漏报率,同时亮点拾取数量减少约40%,且处理耗时减少约37%。

基于LiDAR多维点云优化的垄作菊花采摘机器人自主导航方法研究

[目的]针对田间作业环境复杂导致金丝皇菊采摘机器人行走不稳定、生产效率低的问题,本文设计了一种基于LiDAR多维点云优化的菊花采摘机器人自主导航系统,以实现机器人在农田中的精准作业与高效生产。[方法]通过履带式底盘搭载的Velodyne 16线激光雷达获取田间三维点云信息,并对其进行坐标校正和体素滤波预处理。提出了一种多维点云优化算法,可按照金丝皇菊植株生长特性获取不同坐标轴下的有效点云特征,生成左右两侧垄沟线;并采用改进纯跟踪控制算法对最小二乘法拟合得到导航基准线进行跟踪导航。[结果]通过对Stanley控制算法和改进纯跟踪控制算法进行仿真试验,改进纯跟踪算法表现出更高效的跟踪性能。利用金丝皇菊采摘机器人在南京市湖熟菊花园进行实地试验。试验结果表明,基于LiDAR多维点云优化的自主导航算法横向平均绝对误差为0.0474 m,标准差值为0.0306 m,位置偏差绝对值为0.0789 m,航向平均绝对误差为2.177°,标准差值为2.589°,横向平均绝对误差减小67.13%,离散程度降低48.34%。[结论]本文提出的基于LiDAR多维点云优化算法和改进纯跟踪算法可以有效提高导航精准度,改善系统抗干扰性,导航效果较好,从而保证金丝皇菊采摘机器人的精准作业。

基于PLC的新型桑叶采摘机自动控制系统

传统桑叶采摘主要依靠人工手动方式进行,自动化程度低,劳动成本高,劳动效率低下。为解决这一问题,根据新型桑叶采摘机桑枝定位、桑叶采摘、桑叶输送和桑叶收集4个工作过程,设计自动化采摘装置和基于三菱FX3U系列PLC控制器的自动控制系统,并绘制了功能流程图、硬件接线图和步进梯形图。使用GX-Works2和GX-Simulator2软件对控制系统进行编程和仿真测试。结果表明,所设计新型桑叶采摘机自动控制系统能够安全稳定控制采摘装置运行,系统功能完整,操作便利,可以搭载在农用拖拉机或具有自动导航功能的机器人底盘上实现自动化采摘或智能化采摘,具有重要意义。

基于嵌入式系统的智能小型采摘作业机器人

目前采摘作业机器人存在机器人转弯性能较差、自动寻迹能力较弱以及自动避障能力欠佳的缺陷。针对这一问题,提出一种通过结合嵌入式系统的双差速驱动机器人。首先建立对应的闭环控制模型,嵌入式系统能够进行更为便捷的图像数据信息的采集,而后建立相应的运行模型,并通过超声波传感器进行避障处理。通过嵌入式系统的信息反馈以及机器人的双差速驱动控制,以达到避开障碍物进行自动寻迹和移动的目的。经过实际案例测试,该方案能够在保证路径寻迹足够高精度的同时,更加快捷的避开障碍物,并缩短整体路径行进的实际,具备更好的可靠性和高效性。

基于多策略融合的自动摘酒系统的设计

针对目前我国摘酒工艺自动化程度低、稳定性差、方式单一、无统一量化标准等问题,设计了一种多策略融合的自动摘酒系统。该系统以PLC为核心控制器,触摸屏为人机交互单元,通过在线检测和处理酒精浓度、流速、温度等参数,结合糟源信息自动选择相应的摘酒策略,控制伺服电机驱动分酒管,将酒液分配至不同接酒口,实现全自动摘酒过程。通过旁路采样检测以及酒精度异常值剔除,酒精度实际检测精度达到±0.5%(体积分数),累积流量的检测精度达到±1 L。基于此设计加工样机,在四川泸州某浓香型酒厂进行了测试,结果表明,自动摘酒与人工摘酒在不同糟型及不同分段依据下的相对误差均小于2%(体积分数),说明该系统稳定性强,能适应实际酿造过程。

基于深度学习的地震叠加速度谱自动拾取算法研究

为解决人工拾取地震叠加速度谱时耗时长、效率低等问题,本文提出了一种基于深度学习的地震速度谱自动拾取算法模型VSAP(Velocity Spectrum Accurate Pickup).该算法运用卷积神经网络Faster R-CNN模型构建的多分类任务拾取目标能量团,然后将初步拾取后的能量团坐标输入循环神经网络LSTM(Long-Short Term Memory)模型来进行目标能量团拾取时坐标的取舍和微调,最后输出模型分析和调整过的速度谱自动拾取图像.并通过实际的地震数据集拾取结果验证了该算法模型在叠加速度谱复杂信息的干扰中自动、准确拾取速度谱中能量团的能力,同时验证了该模型的准确性以及鲁棒性.经过改进,该算法模型有效地提高了速度谱拾取的效率和拾取精度.

果蔬机器人采摘中基于改进SDD模型的目标自动化识别研究

为了提高果蔬机器人采摘的识别准确率,利用SDD网络进行实时检测。对基础网络的构造和检测层默认框进行改进,增加卷积层数量,减少计算参数,使其利于对不同尺度的果蔬进行识别,得到基于改进SDD模型的目标自动化识别框架。分析激活函数,发现Relu函数具有更快的收敛速度,利用Relu函数进行激活操作,并对训练结果进行分析,结果表明,改进后的SDD模型具有更快的收敛速度;当置信度为0.45时,改进后的SDD模型具有较理想的准确率、召回率、FI值和IOU值,可以满足果蔬机器人采摘的识别要求。

翻车机传统配车模式优化的可行性研究

武钢有限炼铁厂烧结区域一原料和二原料共有4台翻车机采用传统配车模式,向翻车机内配重车时采用火车机车顶推方式,需要人工进行摘钩作业,缺乏安全联锁保护,自动化水平低,作业效率低。通过翻车机配车系统智能化改造,增加拨车机和自动摘钩机器人、空车推车器等机械化、智能化设备,取代火车机车配位和人工摘钩作业,有效降低安全风险、提升工作效率、降低运营成本。

一种自动隔板取放装置

本文介绍一种自动隔板取放装置,用于商用车U型纵梁的转运和输送。该装置由气动抓取装置、料框单元、控制系统等组成。本设计针对现有技术的不足,提供了一种结构简单、操作方便且性能稳定的隔板自动取放解决方案。

辣椒穴盘苗自动取苗机构运动学分析与试验

针对新疆半自动移栽机人工取苗效率低、强度大、漏苗率及伤苗率高的问题,该文设计了一种辣椒穴盘苗自动取苗机构。分析了该机构的组成及工作原理,建立了其运动学模型,研究了取苗轨迹形成的机理,在确定优化目标的基础上,基于VB开发了取苗机构辅助分析软件,结合Adams运动仿真软件,通过人机交互分析其主要结构参数变化对取苗针端点运动轨迹的影响,优化取苗针端点运动轨迹,获得一组满足辣椒穴盘苗移栽农艺要求的自动取苗机构参数。虚拟样机运动仿真结果表明优化参数可以满足辣椒穴盘苗取苗工作轨迹要求;设计制作了自动取苗试验台,并利用高速摄影技术对取苗机构运动特性进行试验分析,试验结果表明取苗针实际运动轨迹与理论分析结果一致,取苗试验结果如下:取苗成功率为98.6%,基质损伤率为4.2%,投苗成功率为94.2%,满足机械自动取苗的工作要求,验证了取苗机构设计的准确性与合理性。该研究可为辣椒、番茄等作物全自动移栽机自动取苗机构的研发提供参考。

穴盘苗自动取苗试验系统的研制

为了研究取苗器的工作过程与性能,优化取苗器的结构与运动参数,研制了针对单个穴格的自动取苗试验系统。该系统以PC机和数据采集卡为主要硬件,以LabVIEW为软件开发平台。系统具有点动和自动两种运动控制模式选择、试验参数设置、试验结果记录和分析与存储等功能,具有良好的人机交互界面,能够方便地对自动取苗过程中的位移、速度及取苗器结构参数等进行试验。系统技术参数:位移控制范围为0～100 mm,控制精度为10-3 mm;速度控制范围为0～200 mm·s-1,控制精度为10-3 mm·s-1;作用力测量范围为0～100 N,测量精度为10-5 N。试验结果表明:系统对试验参数的控制与测量具有较高的精度和灵敏性。