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.

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.

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.

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.

Active Source Seismic Identification and Automatic Picking of the P-wave First Arrival Using a Convolutional Neural Network

In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.

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.

Locating Famous Tea’s Picking Point Based on Shi-Tomasi Algorithm

To address the difficulty of locating the picking point of a tea sprout during the intelligent automatic picking of famous tea,this study proposes a method to obtain information on the picking point on the basis of the ShiTomasi algorithm.This method can rapidly identify a tea sprout’s picking point and obtain its coordinates.Images of tea sprouts in a tea garden were collected,and the G-B component of tea sprouts was segmented using the Otsu algorithm.The region of interest was set with the lowest point of its contour as the center.The characteristics of tea buds and branches in the area were extracted,and the Otsu algorithm was used for a second segmentation of tea sprout images.The tea buds were segmented using the improved Zhang algorithm.The branch feature binary image was used to refine the skeleton,and the Shi-Tomasi algorithm was used to detect the corners of the skeleton and calculate and mark the picking points of the shoots.Sixty sets of samples were tested.The test identified 1,042 effective shoots for tender buds,and 887 picking points were marked,with a success rate of 85.12%,thereby verifying the effectiveness of the method and providing a theoretical reference for the visual positioning of the automatic picking of famous tea.

Applying the Method for Solving Traveling Salesman Problem Based on Backtracking Algorithm to Order Picking

In the distribution center, the way of order picking personnel to pick goods has two kinds: single picking and batch picking. Based on the way of the single picking and assumed warehouse model, in order to reduce the walking path of order picking, the order picking problem is transformed into the traveling salesman problem in this paper. Based on backtracking algorithm, the order picking path gets optimized. Finally verifing the optimization method under the environment of VC++6.0, order picking path in the warehouse model get optimized, and compared with the traditional order picking walking paths. The results show that in small and medium-sized warehouse, the optimization method proposed in this paper can reduce order picking walking path and improve the work efficiency as well as reduce the time cost.

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.

Research State and Trend of Fruit Picking Robot Manipulator Structure

Starting from the characteristics of fruit picking,the characteristics of fruit picking robot manipulators and the research state at home and abroad are reviewed.The analysis summarizes the difficulties in fruit picking robotic arm research.Aiming at the configuration of the manipulator,the structure and characteristics of the manipulator with redundant degrees of freedom are introduced,and the feasibility of the redundant mechanism is demonstrated through the current research state of the manipulator.

Intelligent Control Mechanism of Part Picking Operations of Automated Warehouse

This paper studies the part picking operations of a ut omated warehouse. It assumed the demand of picking orders of automated warehouse are dynamic generated. Once the picking orders of certain period of time are kn own, it is necessary to decide an efficient order picking sequence and routing t o minimize the total travel distance to complete those orders. Assumed there are n i items to be picked in order O i. Each item in the picking ord er is located in different locations in the warehouse. Since it is possible the same items appear in the different picking orders, it will reduce the picking di stance if these orders can be batched and picked in one path. However, there are several constraints for the order batching and order picking operations. These constraint are (1) the crane of the automated warehouse has the carrying capacit y of C, and (2) for the management convenience, it is assumed that one picki ng order must be completed in one path. Because of the complexity of problem, it is inefficient to solve the problem by analytical approach. Although the heuristic method can significantly reduce of the computation time, the quality of the solution is always unacceptable. It is the intention of this paper to integrate the advantages of neural network and simulated annealing technique to develop the control mechanism for the planning of order picking operations of automated warehouse. A systematic computational simulation is conducted to evaluate the proposed method. The results show the pr oposed method can generate superior solution in most cased.

Arrival picking method for microseismic phases based on curve fi tting

Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data.As the signal-to-noise ratios of P-and S-waves diff er because of the infl uence of focal mechanisms,geometry,and background noise,diffi culties are introduced in the eff ective discrimination of seismic phases and the accurate acquisition of arrivals in conventional processing methods.In this paper,we propose a method for identifying microseismic phase arrival time by comprehensively analyzing the variation of moveout curves and combining the intra-event waveform similarities with the energy ratio of multitrace signals.First,a curve-fi tting formula is constructed with perforation arrivals,and event detection is achieved by adopting an energy-weighted similarity coeffi cient that seeks plausible fi tting curves with a sliding time window in continuous microseismic recordings.Then,the P-and S-waves are separated by the fitting parameters.The known arrival time trend of the microseismic phase is employed to calculate residual time corrections.Finally,the accurate arrival results of the microseismic phases can be obtained by picking the arrivals of stacked traces.The reliability and eff ectiveness of the proposed method for microseismic phase detection and arrival picking were determined through tests using field data.Arrival results indicate that the proposed method can improve accuracy compared with the traditional energy ratio method.

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.

Urdnet:A Cryo-EM Particle Automatic Picking Method

Cryo-Electron Microscopy(Cryo-EM)images are characterized by the low signal-to-noise ratio,low contrast,serious background noise,more impurities,less data,difficult data labeling,simpler image semantics,and relatively fixed structure,while U-Net obtains low resolution when downsampling rate information to complete object category recognition,obtains highresolution information during upsampling to complete precise segmentation and positioning,fills in the underlying information through skip connection to improve the accuracy of image segmentation,and has advantages in biological image processing like Cryo-EM image.This article proposes A U-Net based residual intensive neural network(Urdnet),which combines point-level and pixel-level tags,used to accurately and automatically locate particles from cryo-electron microscopy images,and solve the bottleneck that cryo-EM Single-particle biologicalmacromolecule reconstruction requires tens of thousands of automatically picked particles.The 80S ribosome,HCN1 channel and TcdA1 toxin subunits,and other public protein datasets have been trained and tested on Urdnet.The experimental results show that Urdnet could reach the same excellent particle picking performances as the mainstream methods of RELION,DeepPicker,and acquire the 3Dstructure of picked particleswith higher resolution.

P wave onset time picking with the B-spline biorthogonal wavelet

The seismic wave consists of many seismic phases, which contain rich geophysical information from the hypocenter, medium of seismic wave passing through and so on. It is very important to detect and pick these seismic phases for understanding the mechanism of earthquake, the Earth structure and property of seismic waves. In order to reduce or avoid the loss resulted from the earthquake, one of the important goals of seismic event detecting is to obtain its related information before and after it occurs. Because of the particularity of P wave and S wave the seismic event detecting focuses on distinguishing P and S waves and picking their onset time, it has been becoming one of the research hotspots for many geophysicists to pick the P and S wave arrival accurately and effectively.

Comparative study of The ship of Death And After Apple—picking——The Differences Under the Theme of Deach

Though Frost and Laurence carry some similiar notes in the ar poems,this can not obscure their differenccs. Frist.Frost’s After APPle-picking can be regarded as the exampleof that dominant mode of the oppening 25 years of the century.It is aquietly reflective pome of the actual New England farmer’s life.HereForst takes the mask of a New England farmer and speaks to

THE COMPARATIVE STUDY OF "THE SHIP OF DEATH" AND "AFTER APPLE—PICKING"——The Similiar Notes

After Apple-picking is one of the representatives of Robert’ Forst’s masterpieces. It was published in 1914. The speaker in this poem is a New England farmer.The time is in the late autumn.The poem begins with the farmer’s description of his working tool with loving exactness____a "long twopointed ladder","a barrel".Besides these,the farmer has also introduced for us apples on the bough, a drinking trough, the frosty grass,the

CRPN: A cascaded classification and regression DNN framework for seismic phase picking

Current deep neural networks(DNN)used for seismic phase picking are becoming more complex,which consumes much computing time without significant accuracy improvement.In this study,we introduce a cascaded classification and regression framework for seismic phase picking,named as the classification and regression phase net(CRPN),which contains two convolutional neural network(CNN)models with different complexity to meet the requirements of accuracy and efficiency.The first stage of the CRPN are shallow CNNs used for rapid detection of seismic phase and picking P and S arrival times for earthquakes with magnitude larger than 2.0,respectively.The second stage of CRPN is used for high precision classification and regression.The regression is designed to reduce the time difference between the probability maximum and the real arrival time.After being trained using 500,000 P and S phases,the CRPN can process 400 hours’seismic data per second,whose sampling rate is 1 Hz and 25 Hz for the two stages,respectively,on a Nvidia K2200 GPU,and pick 93%P and 89%S phases with the error being reduced by 0.1s after regression correction.

Augmented Reality for Realistic Simulation Using Improved Snake and Picking Algorithm by Proportional Relational Expression

In realistic simulation of mobile Augmented Reality, essential point is how to best depict occluded area in such a way that the user can correctly infer the depth relationships between real and virtual objects. However, if the constructed 3D map of real world is not accurate or the density is not sufficient to estimate the object boundary, it is very difficult to determine the occluded area. In order to solve this problem, this paper proposes a new method for calculating the occlusion area using the improved snake algorithm and picking algo-rithm by the proportional relational expression. First, we generated the wireframe by the DEM in the experimental region and mapped to CCD real image using visual clues. And then, we calculated the 3D information at the point where occlusion problem for a moving virtual target by the proposed method. Experimental results show the validity of the proposed approach under the environment in which partial occlusions occur.