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.

Automatic velocity picking based on optimal key points tracking algorithm

Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.

First-Arrival Picking Method for Active Source Data with Ocean Bottom Seismometers Based on Spatial Waveform Variation Characteristics

The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.

Manual Order Picking Route Optimization in Distribution Warehouse of Chain Furniture Retail Enterprise

Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.

COMPRESSIBLE VIRTUAL WINDOW ALGORITHM IN PICKING PROCESS CONTROL OF AUTOMATED SORTING SYSTEM

Compared to fixed virtual window algorithm （FVWA）, the dynamic virtual window algorithm （DVWA） determines the length of each virtual container according to the sizes of goods of each order, which saves space of virtual containers and improves the picking efficiency. However, the interval of consecutive goods caused by dispensers on conveyor can not be eliminated by DVWA, which limits a further improvement of picking efficiency. In order to solve this problem, a compressible virtual window algorithm （CVWA） is presented. It not only inherits the merit of DVWA but also compresses the length of virtual containers without congestion of order accumulation by advancing the beginning time of order picking and reasonably coordinating the pace of order accumulation. The simulation result proves that the picking efficiency of automated sorting system is greatly improved by CVWA.

ORDER-PICKING OPTIMIZATION FOR AUTOMATED PICKING SYSTEM WITH PARALLEL DISPENSERS

Based on the characteristics of parallel dispensers in automated picking system, an order-picking optimization problem is presented. Firstly, the working principle of parallel dispensers is introduced, which implies the time cost of picking each order is influenced by the order-picking sequence. So the order-picking optimization problem can be classified as a dynamic traveling salesman problem （TSP）. Then a mathematical model of the problem is established and an improved max-min ant system （MMAS） is adopted to solve the model. The improvement includes two aspects. One is that the initial assignment of ants depends on a probabilistic formula instead of a random deployment; the other is that the heuristic factor is expressed by the extra picking time of each order instead of the total. At last, an actual simulation is made on an automated picking system with parallel dispensers. The simulation results proved the optimization value and the validity of improvement on MMAS.

Fluid-based Slotting Optimization for Automated Order Picking System with Multiple Dispenser Types

Slotting strategy heavily influences the throughput and operational cost of automated order picking system with multiple dispenser types, which is called the complex automated order picking system （CAOPS）. Existing research either focuses on one aspect of the slotting optimization problem or only considers one part of CAOPS, such as the Low-volume Dispensers, to develop corresponding slotting strategies. In order to provide a comprehensive and systemic approach, a fluid-based slotting strategy is proposed in this paper. The configuration of CAOPS is presented with specific reference to its fast-picking and restocking subsystems. Based on extended fluid model, a nonlinear mathematical programming model is developed to determine the optimal volume allotted to each stock keeping unit （SKU） in a certain mode by minimize the restocking cost of that mode. Conclusion from the allocation model is specified for the storage modules of high-volume dispensers and low-volume dispensers. Optimal allocation of storage resources in the fast-picking area of CAOPS is then discussed with the aim of identifying the optimal space of each picking mode. The SKU assignment problem referring to the total restocking cost of CAOPS is analyzed and a greedy heuristic with low time complexity is developed according to the characteristics of CAOPS. Real life application from the tobacco industry is presented in order to exemplify the proposed slotting strategy and assess the effectiveness of the developed methodology. Entry-item-quantity （EIQ） based experiential solutions and proposed-model-based near-optimal solutions are compared. The comparison results show that the proposed strategy generates a savings of over 18% referring to the total restocking cost over one-year period. The strategy proposed in this paper, which can handle the multiple dispenser types, provides a practical quantitative slotting method for CAOPS and can help picking-system-designers make slotting decisions efficiently and effectively.

Taboo Search Algorithm for Item Assignment in Synchronized Zone Automated Order Picking System

The idle time which is part of the order fulfillment time is decided by the number of items in the zone; therefore the item assignment method affects the picking efficiency. Whereas previous studies only focus on the balance of number of kinds of items between different zones but not the number of items and the idle time in each zone. In this paper, an idle factor is proposed to measure the idle time exactly. The idle factor is proven to obey the same vary trend with the idle time, so the object of this problem can be simplified from minimizing idle time to minimizing idle factor. Based on this, the model of item assignment problem in synchronized zone automated order picking system is built. The model is a form of relaxation of parallel machine scheduling problem which had been proven to be NP-complete. To solve the model, a taboo search algorithm is proposed. The main idea of the algorithm is minimizing the greatest idle factor of zones with the 2-exchange algorithm. Finally, the simulation which applies the data collected from a tobacco distribution center is conducted to evaluate the performance of the algorithm. The result verifies the model and shows the algorithm can do a steady work to reduce idle time and the idle time can be reduced by 45.63% on average. This research proposed an approach to measure the idle time in synchronized zone automated order picking system. The approach can improve the picking efficiency significantly and can be seen as theoretical basis when optimizing the synchronized automated order picking systems.

Comparative experimental analysis on various picking types under dedicated and random storage assignments for automated storage and retrieval systems

Product storage policy, single picking volume and picking routing are the three factors of vital importance that affect the efficiency of a crane to pick goods in automated storage and retrieval systems(AS/RS). Comparative experiments on picking efficiency were conducted targeting picking operation with order of 1 to 20. Based on dedicated and random storage policies, 4 picking methods of patching-based, S-type, return-type and optimized-type routes were used and compared in the experiments. The results show that either the dedicated policy or the random policy was applied, crane worked most efficiently with optimizedtype route, followed by S-type path, patching-based path, and return-type path. When the number of orders in a single picking is larger(more than 5), the random storage policy is preferable to the dedicated policy.

USTC-Pickers:a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.

Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.

Pick-up strategies for and electrical characterization of ZnO nanowires with a SEM-based nanomanipulator

Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two pick-up strategies are proposed for characterizing the electrical properties of ZnO nanowires using SEM equipped with a nanomanipulator.To pick up nanowires efficiently,direct sampling is compared with electrification fusing,and experiments show that direct sampling is more stable while electrification fusing is more efficient.ZnO nanowires have cut-off properties,and good Schottky contact with the tungsten probes was established.In piezoelectric experiments,the maximum piezoelectric voltage generated by an individual ZnO nanowire was 0.07 V,and its impedance decreased with increasing input signal frequency until it became stable.This work offers a technical reference for the pick-up and construction of nanomaterials and nanogeneration technology.

Motion Planning System for Bin Picking Using 3-D Point Cloud

In this paper, we propose a motion planning system for bin picking using 3-D point cloud. The situation that the objects are put miscellaneously like the inside in a house is assumed. In the home, the equipment which makes an object stand in line doesn’t exist. Therefore the motion planning system which considered a collision problem becomes important. In this paper, Information on the objects is measured by a laser range finder (LRF). The information is used as 3-D point cloud, and the objects are recognized by model-base. We propose search method of a grasping point for two-fingered robotic hand, and propose search method of a path to approach the grasping point without colliding with other objects.

MMP15、PICK1基因在山羊睾丸中的表达研究

MMP15、PICK1基因均是哺乳动物性腺组织中影响性腺生理功能正常发挥的重要影响因子,但它们在黔北麻羊睾丸组织中的表达规律尚不明晰。为研究黔北麻羊不同月龄的睾丸中MMP15、PICK1基因的表达情况,本试验以黔北麻羊为研究对象,通过qRT-PCR技术分别检测MMP15与PICK1基因在1、3、6、9、12月龄的黔北麻羊睾丸中的表达水平并建立基因表达的标准曲线。结果显示:MMP15、PICK1基因在黔北麻羊不同月龄睾丸组织中均有表达;MMP15基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列为:12月龄>1月龄>9月龄>3月龄>6月龄;12月龄与1月龄的表达差异不显著(P>0.05),二者表达量均极显著高于3月龄、6月龄、9月龄(P<0.01);9月龄表达显著高于3月龄(P<0.05),极显著高于6月龄(P<0.01);3月龄表达极显著高于6月龄(P<0.01)。PICK1基因在黔北麻羊不同月龄睾丸中的表达量从高到低排列分别是:3月龄>6月龄>12月龄>9月龄>1月龄;3、6月龄其表达量极显著高于9、12月龄(P<0.01);1月龄与其他几个月龄相比,其表达量均极显著低于其他月龄(P<0.01)。结合黔北麻羊的生理机能发育情况,黔北麻羊在6月龄时已经基本达到性成熟,所以此时睾丸内细胞活动增强,睾丸机能活跃。本次试验结果表明,黔北麻羊睾丸生精功能的正常发挥或与MMP15及PICK1基因的表达量有关。

Truncating PICK1 Variant Identified in Azoospermia Affected Mitochondrial Dysfunction in Knockout Mice

Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually disrupts acrosome formation and leads to male infertility.Methods An azoospermia sample was filtered,and the laboratory detection and clinical phenotype indicated typical azoospermia in the patient.We sequenced all of the exons in the PICK1 gene and found that there was a novel homozygous variant in the PICK1 gene,c.364delA(p.Lys122SerfsX8),and this protein structure truncating variant seriously affected the biological function.Then we constructed a PICK1 knockout mouse model using clustered regularly interspaced short palindromic repeat cutting technology(CRISPRc).Results The sperm from PICK1 knockout mice showed acrosome and nucleus abnormalities,as well as dysfunctional mitochondrial sheath formation.Both the total sperm and motility sperm counts were decreased in the PICK1 knockout mice compared to wild-type mice.Moreover,the mitochondrial dysfunction was verified in the mice.These defects in the male PICK1 knockout mice may have eventually led to complete infertility.Conclusion The c.364delA novel variant in the PICK1 gene associated with clinical infertility,and pathogenic variants in the PICK1 may cause azoospermia or asthenospermia by impairing mitochondrial function in both mice and humans.

Niemann-Pick A/B Disease in a 13-Year-Old Child and Review of the Literature

Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insufficiency pulmonary and/or central nervous system (CNS). Thanks to the pioneering work of Roscoe Brady and colleagues, we now know that there are two distinct metabolic abnormalities that explain NPD. The first is due to the deficient activity of the acid sphingomyelinase enzyme (ASM;NPD “types A and B”), and the second is due to defective functioning in the transport of cholesterol (NPD “type C”). We report the case of a 13-year-old adolescent diagnosed with Niemann-Pick A/B disease.

PICK1参与调控芬太尼诱导的大鼠痛觉过敏机制的研究

目的:探讨与蛋白激酶C相互作用的蛋白质1(PICK1)参与调控阿片类药物诱导的痛觉过敏(OIH)的分子机制。方法:颈部皮下注射枸橼酸芬太尼建立大鼠OIH模型,通过PCR实验检测Pick1在大鼠背根神经节(DRG)中表达水平;构建Pick1的过表达载体,包装pLV-Pick1重组慢病毒后对大鼠进行鞘内注射,电生理测定大鼠DRG神经元兴奋性变化,观察慢病毒注射前(T0)、慢病毒表达后(T1)和芬太尼给药后(T2)大鼠机械痛阈。结果:与Control组相比,OIH组大鼠的机械痛阈明显降低;Pick1在OIH大鼠DRG中表达量有明显下调;芬太尼孵育后DRG细胞的动作电位的阈电流明显降低,鞘内注射pLV-Pick1慢病毒的大鼠DRG细胞在芬太尼孵育后,阈电流相无显著下调。结论:在DRG水平上,Pick1可参与调控痛觉过敏,在DRG上干涉Pick1的表达,可以缓解芬太尼诱导的痛觉过敏。

采摘机器人机械手避障方法研究——基于遗传优化和模糊PID控制器

为了提高采摘机器人机械手移动的自动化程度,使其在移动过程中有效避开障碍物,将基于PID控制器的避障系统引入到了机械手的设计上,并采用遗传算法和模糊算法对控制器进行了优化,提高了机械手移动的准确性和平稳性。模拟采摘机器人机械手的作业环境,对机械手的避障功能进行了测试,结果表明:机械手可以成功躲避障碍物,且采用遗传和模糊算法优化的机械手具有更高的移动平稳性,对于提高果实采摘效率和质量具有重要的作用。

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%。

基于GPC-ILC的秸秆捡拾致密成型机主轴转速控制方法研究

为解决秸秆捡拾致密成型机主轴转速自动控制问题,以利于致密成型机全程智能化作业,设计了电液控制系统的数学模型与转速预测模型,提出了一种基于GPC-ILC的致密成型机主轴转速控制方法,通过采集先前成型机运行过程中的输入、输出数据,使用带遗忘因子的最小二乘法辨识广义预测控制的参数模型并计算预测输出值,根据以往过程的累计平均模型误差修正预测输出值,并引出迭代学习控制律,在线实时计算新的控制量,实现主轴转速的控制。场地收获试验表明:增负荷时,转速最大动态偏差为3.21 r/min,与目标值的偏差为2.6%,最大余差为1.23 r/min;减负荷时,最大动态偏差为2.23 r/min,与目标值的偏差为2.47%,最大余差为0.89 r/min;增减负荷转速达到稳定时间小于5 s,超调量小于3%。田间试验表明:最大动态偏差为3.75 r/min,与目标值的偏差为3.47%,最大余差为1.79 r/min,满足成型机田间作业的需求。GPC-ILC算法可及时校正模型失配、干扰引起的转速控制的不确定性。