Perfect Pick系统订单拣选策略优化研究

作为电商仓库作业中耗时最长、成本最高的环节,订单拣选的作业效率一直是仓库管理中的关键问题。文章针对拣选策略选择问题,选择Perfect Pick系统的电商仓库作为背景,对单个拣选台进行研究,提出订单分批和订单排序两种拣选策略并进行对比分析;以货箱搬运次数最少为目标,分别构建订单分批与订单排序两种拣选策略的整数规划模型,并设计贪婪算法进行求解。通过数值实验验证,订单排序策略在所有订单规模中均优于订单分批策略,根据仓库的订单规模,合理设置拣选台最大容量并选择合理的拣选策略,能够更好地提高拣选效率、优化仓库作业环节。

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.

Niemann-Pick C1蛋白在埃博拉病毒感染中的作用及其靶向药物研究进展

埃博拉病毒属丝状病毒科,具有高传染性,能引起人类和灵长类动物出现严重出血热等症状,病死率高达90%。Niemann-Pick C1(NPC1)蛋白是埃博拉病毒感染过程中表达于宿主细胞内体膜上的一个重要受体,其与埃博拉病毒被组织蛋白酶裂解的糖蛋白(GP)的相互作用是病毒感染宿主的关键环节,介导病毒囊膜与内体膜的融合,进而将病毒基因组释放到宿主细胞。近年来,将NPC1蛋白作为广谱抗丝状病毒药物靶点研发的小分子抑制剂、单克隆抗体和基因治疗药物均有突破性进展。本文介绍了NPC1的结构及其在埃博拉病毒感染中的作用,并对靶向NPC1的小分子抑制剂、单克隆抗体药物和基因治疗药物的研究现状进行总结。

Intelligent evaluation of mean cutting force of conical pick by boosting trees and Bayesian optimization

Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.

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.

Basic Research on the Burden of Dropping off and Picking up Children: Differences by Employment Type and Gender in the Tokyo Metropolitan Area

There is a need to reduce the burden of child drop-off and pick-up for child-rearing generations, but most studies on the actual situation in Japan are based on survey results. In this study, we analyzed differences in child drop-off and pick-up by employment type and gender, utilizing the “Metropolitan Area Person Trip Survey,” which is a statistical data set. The study targeted households in which both spouses were between 30 and 49 years old, had children under the age of 6, and included the following three groups. 1) Dual-income Group 1 (both spouses employed/on contract/temporary);2) Dual-income Group 2 (husband employed/on contract/temporary, wife part-time);3) Full-time housewife group (husband employed, wife unemployed). The analysis revealed that a) wives are almost always responsible for dropping off and picking up their children;b) husbands drop off and pick up their children less frequently in dual-income households;and c) households with children raising within 10 to 30 km of Tokyo Station have longer commuting times and need to reduce the burden of dropping off and picking up their children.

基于双目视觉技术的茶叶嫩芽定位方法研究

进一步提升茶叶嫩芽定位的准确性及茶叶嫩芽采摘的作业效率,选取双目视觉技术作为切入点,针对其识别、定位、采摘等环节展开设计研究。考虑茶叶嫩芽采摘的基础特征和双目视觉技术的应用条件,合理搭建双目视觉模型,选用立体匹配的核心定位算法,完成茶叶嫩芽的定位方法与采摘功能优化,并布置相应的试验机构,展开定位采摘验证试验。结果表明:基于双目视觉技术的茶叶嫩芽定位方法应用具有显著的优越性,较普通定位方法而言,识别误匹配率可降低至4.01%,平均漏采率为3.20%,嫩芽采摘完整率达93.40%,可以很好地促进茶叶嫩芽的后续处理加工,是采摘茶叶嫩芽的品质化的重要基础保障。

大棚蔬菜采摘运输设备的研究与设计

【目的】目前大棚蔬菜采摘设备存在人工劳动强度大、效率低、使用受限等问题,因此亟需研发一款性价比高的大棚蔬菜采摘助力装备。【方法】在大量实地调研和实践的基础上,设计了一种基于单片机控制的新型大棚蔬菜采摘运输设备。该设备主要由换垄机构、张紧机构、传送机构和支撑机构四部分组成,分析了该设备的总体设计思路,并详细阐述了各机构设计及关键技术和创新点。【结果】该运输设备具有结构简单、造价低、使用方便、机械化程度高、适应性强等特点,换垄机构不仅能实现宽度可调以适应不同垄宽,而且可以通过链传动实现机械换垄,同时采用“对对门”支撑,能有效防止钢丝绳下垂,方便菜筐通过。【结论】该大棚蔬菜采摘运输设备能极大程度降低人工劳动强度,提高采摘效率,具有很好的市场前景。未来还将研发自主识别采摘设备,实现大棚蔬菜采摘的全自动化,真正实现从“多人管一棚”到“一人管多棚”的转变。

基于BlendMask与改进Super4PCS的物体位姿估计方法

针对工业上常见的散乱堆叠工件的位姿估计和无序分拣问题,文章提出一种基于BlendMask实例分割算法与改进Super 4PCS(super 4-points congruent sets)点云配准算法的物体位姿估计方法。利用BlendMask实例分割算法分割目标工件并构建工件表面点云;定义抓取分数选择最上层工件作为抓取对象,实现机器人自上而下抓取;针对不同形状的目标工件,基于相似匹配度函数选择模板点云;融合Super 4PCS与正态分布转换(normal distributions transform,NDT)算法完成目标点云和模板点云在低重合率情况下的精确配准,获取目标工件的抓取位姿;并针对Super 4PCS算法粗配准时速度较慢的问题,通过对体素滤波后的目标点云和模板点云提取3D内部形状描述子(intrinsic shape signatures,ISS)作为点云配准算法的输入点集来提高算法的配准速度。点云配准实验和机器人无序分拣实验结果表明,文章提出的物体位姿估计方法可以快速、准确地完成目标工件的位姿估计和无序分拣任务,配准算法均方根误差(root mean square error,RMSE)小于3.8 mm,位姿估计所需时间小于5.8 s,性能满足实际应用要求。

Pick病的诊断

目的 探讨Pick病 (Pickdisease)诊断的正确性。方法 分析 2例临床上曾诊断为阿尔茨海默病(Alzheimerdisease,AD) ,经核磁共振成像 (MRI)检查证实的Pick病 ,其中 1例经正电子发射型计算机断层扫描 (PET)得到进一步证实。结果 Pick病与AD比较有如下特点 :①特殊的语言方式及语言改变 ;②临床上以缓慢进展的性格改变及社会活动能力衰退为主 ,而记忆力、理解判断力、计算力障碍等出现相对较晚 ;③病程中有不同程度的Kl櫣ｖｅｒ Bucy综合征表现 ;④脑MRI检查提示 :额和 (或 )颞叶萎缩 ,颞极为著 ;⑤PET检查提示 :双侧额、颞叶代谢减低 ,颞极明显。结论 根据Pick病临床表现及神经影像学特征 ,在排除AD及其它脑变性病的基础上 ,生前诊断Pick病是可能的。

Pick病的脑MRI研究

目的 研究Pick病患者脑萎缩变化的特点 ,并动态研究脑萎缩变化的程度和病程及临床之间的相互相关系。方法 2例Pick病患者分别进行 5次和 7次脑MRI动态观察 ,并和 6例Alzheimer病患者进行对比分析。结果 Pick病早期以Kluver Bacy综合征和情感障碍为突出 ,人格改变、行为异常、语言障碍为显著 ,而智能减退相对较轻较晚 ,脑MRI以额或 /和颞极萎缩为主 ,尤其颞上回前 1/ 3为明显 ,其脑MRI的改变和临床特征及病程相一致。“刀片”状、“剪刀”像和“锥柱”样或“蘑菇”状脑萎缩改变可能是Pick病的特点。结论 充分认识这些特征 ,以利于Pick病的早期临床诊断。

Pick病3例临床及病理分析

通过分析 3例Pick病的临床、脑MRI、SPECT、PET和脑病理资料 ,发现和Alzheimer病相比 ,Pick病早期以人格变化、判断力改变较为特异 ,情感、情绪变化明显 ,Kl櫣ｖｅｒ Bucy综合征为特征 ,非流利性失语和语义性记忆障碍突出 ,而智能减退及视觉空间定向能力受损相对较晚 ,脑MRI以颞极萎缩为重 ,SPECT和PET以额和 (或 )颞叶低灌流及低代谢为显著。提示应充分认识Pick病的特点 。

PICK1蛋白生理功能及其作为药物新靶点研究进展

PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。

PICK1:一个功能多样的药靶蛋白

蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功能多样以及在多种重要疾病(如:癌症、精神分裂症、疼痛、帕金森综合症等)的发生发展过程中发挥潜在的作用,使其成为一个可能的药靶蛋白.PICK1与绝大多数配体蛋白的相互作用是通过其PDZ结构域与配体C末端区域的结合介导的,使PICK1的PDZ结构域成为一个潜在的药物靶点.因此,可以利用生物小分子物质特异性地结合PICK1的PDZ结构域,干扰或阻断PICK1与配体蛋白的天然相互作用,最终达到治疗相关疾病的目的.

矩阵值Caratheodory函数广义块Pick矩阵的秩不变性

Lasarow推导出矩阵值Caratheodory函数的第一、第二型广义块Pick矩阵及其变型的秩不变性.这些矩阵由同一个Caratheodory函数的值与它的直到某阶的导数值确定.利用文献中提出的块Toeplitz向量方法,该文断言,这些块矩阵的秩分别相关并重合于具有秩不变性的块Toeplitz矩阵的秩,从而改进了这两类广义块Pick矩阵的秩不变性结论的证明.

广义块Pick型矩阵和带导数的Nevanlinna-Pick矩阵插值问题

建立了广义块Pick型矩阵和块Toeplitz矩阵之间的一种等价关系。

Landscape Design of Sightseeing and Picking Garden Transformed from Vegetable Producing Garden

In view of landscape design problems in the transition from vegetable producing garden to sightseeing and picking garden,definitions of both gardens were introduced and discriminated.It was proposed that landscapes in the vegetable sightseeing and picking garden included installations,open-field vegetable producing landscapes and overall environment landscapes.Landscape design concepts and principles of vegetable sightseeing and picking garden were analyzed,and it was stressed that its landscape design should take quality production of vegetables and fruits as the principal line,environment landscapes of the garden as the support,and experiencing production process as the feature,by following the principles of "integrity of garden design,characteristic vegetable varieties,proper crop rotation,ecological production process".Landscape contents of this garden were analyzed from 3 perspectives:landscape design within installations,major road,and overall appearance of the garden.Cangshang Vegetable Sightseeing and Picking Garden in Beiwu Township,Shunyi District,Beijing City was taken for an example to analyze its landscape construction inside and outside greenhouses as well as the optimization of the overall environment landscapes on the basis of introducing its landscape design concepts.

额颞痴呆与Pick病

额颞痴呆(frontotemporal dementia,FTD)是以额颞叶萎缩为特征的一组神经变性疾病。病因认为是轴索病变继发神经细胞改变,机制为常染色体显性遗传。病理为双侧额叶,颞叶前端的局限性萎缩,可累及尾状核、壳核、丘脑、黑质等皮质下结构。起病隐袭,早期症状为行为、人格改变,渐进性发展为认知功能损害,迅速进展为语言障碍,晚期通常沉默不语,不能与人交流,可有行为异常。MRI显示额叶、前颞叶萎缩。确诊依赖于病理学证据,如局限性额颞叶萎缩,神经元和神经胶质发现tau蛋白包涵体及Pick小体细胞。目前无特异性治疗的治疗方法。