YOLO-Based Damage Detection with StyleGAN3 Data Augmentation for Parcel Information-Recognition System

Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.

Shape Analysis of Agricultural Parcels for Land Consolidation Priorities in Tekirdag Province,Turkey

Shape irregularity,a sub-factor of parcel fragmentation is a problem that hinders sustainable agriculture and is solved using land consolidation projects.Determination of the parcel shape degree contributes significantly to spatial prioritization where there is also a high probability of achieving positive effects of consolidation projects.This study aims to determine the shape degree of the agricultural parcels both at singular and rural county scales in Tekirdag Province,Turkey in 2020 by combining the parcel shape index(PSI) with the minimum bounding geometry index(MBG) to improve parcel scores.Hot-spot zones of the highly irregular and near optimum parcels were also determined using Getis-Ord G_(i)^(*) statistic.The parcel degrees were classified into four categories,namely highly irregular,irregular,regular and near optimum.The obtained unweighted scores of the parameters exhibit deviations from the expected values.After weighting by pairwise comparison,the values approached ideal scores.Among 346 740 parcels,53% were highly irregular and irregular and 47% were regular and near optimum shapes after weighting whereas these were 70% and 30%,respectively before weighting.The average parcel degree of 63 rural counties was regular while the average parcel degree of the remaining 264 rural counties was irregular.The combined use of PSI and MBG index improved the correctness of the parcel shape score.It could be suggested to use as a tool in land consolidation prioritization.

Abandoned land identification in karst mountain area based on time series SAR characteristics at geo-parcels scale

Mapping abandoned land is very important for accurate agricultural management.However,in karst mountainous areas,continuous high-resolution optical images are difficult to obtain in rainy weather,and the land is fragmented,which poses a great challenge for remote sensing monitoring of agriculture activities.In this study,a new method for identifying abandoned land is proposed:firstly,a few Google Earth images are used to transform arable land into accurate vectorized geo-parcels;secondly,a time-series data set was constructed using Sentinel-1A Alpha parameters for 2020 on each farmland geoparcel;thirdly,the semi-variation function(SVF)was used to analyze the spatial-temporal characteristics,then identify abandoned land.The results show:(1)On the basis of accurate spatial information and boundary of farmland land,the SAR time-series dataset reflects the structure and time-series response.abandoned land with an accuracy of 80.25%.The problem of remote sensing monitoring in rainy regions and complex surface areas is well-resolved.(2)The spatial heterogeneity of abandoned land is more obvious than that of cultivated land within geoparcels.The step size for significant changes in the SVF of abandoned land is shorter than that of cultivated land.(3)The SVF time sequence curve presented a strong peak feature when farmland was abandoned.This reveals that the internal spatial structure of abandoned land is more disordered and complex.It showed that time-series variations of spatial structure within cultivated land have broader applications in remote sensing monitoring of agriculture in complex imaging environments.

高性能并行PIM系统中Parcels通信机制研究

基于Processor-in-Memory(PIM)技术的高性能并行系统具有可扩展性、自适应性、鲁棒性和低功耗等特性.借助于Parcels通信机制,并行PIM系统可以实现消息驱动的计算,重叠计算与通信,降低通信系统对细粒度并行应用的影响,可充分利用PIM的内部带宽和应用局部性.文章对并行PIM系统中采用的Parcels通信机制及其特点、Parcels通信模型,以及Parcels的典型应用系统进行了着重介绍,对存在的问题进行了分析并指出了进一步的研究方向.

On the Development of Meso-Scale Heavy Rain Parcels in China

Heavy rains occur in China frequently, which often bring us floods and serious disasters in the summer half-year. The meso-scale heavy rain parcels (MHRP) in the mid-latitude are usually developed in following cases:I.By the approaching, meeting and / or overlapping of different weather systems, when two or more different rainfall systems are getting to conjugate, some MHRPs could be developed, such as: 1) a new cold/warm front or squall line approaches an old front or squall, even when the old one is somewhat decrepit; 2) at the places where two or more synoptic systems with different characteristics are meeting together, such as the meeting of tropical cyclone with the cold airs coming from the mid- and / or high-latitudes, or the low latitude vortex meeting with the westerly trough; 3) at the intersections of some different weather systems, such as the intersection of drylines, squall lines or fronts moving from different directions; and 4) by the overlapping of rainfall parcels produced continuously from a meso-generation centre.II.Resonance Effect and Tibetan Plateau Influence are two reasons why high frequency of heavy and torrential rains arround the meiyu front is discussed also.

Building a Geodatabase for Parcel and Cadaster Mapping and Add-Ins Development: A Case Study for Defense Housing Authority (DHA), Lahore, Pakistan

This paper presents a systematic view of most up-to-date tools, technologies and techniques which is appropriate not only for effective decision making, but also helpful for competent administration to provide efficient resolution for spatial troubles of Khasra and Parcel management. Different Data collection techniques have been applied on different data sources. Worldview II of 0.5 resolution image, scanned Master Plan and Cadastral/Khasra Maps was used for development of geodatabase for spatial and non-spatial entities using ArcGIS 10.2. Multiple processing on Satellite Imagery using ERDAS Imagine was performed like Image enhancement, Mosaicking and Color Balancing, Image Geo-referencing and Raster Cataloging. Development of Customize Graphical User Interface (GUI) for DHA’s officials and administrator makes efficient decision in society matters. This is the first spatial GUI, for DHA to plan and maintain society’s activities efficiently, in the form of ArcMap Add-Ins. These Add-Ins are written in C#.Net using ArcObjects APIs within MSVS 2012.

An Analysis of the Air Parcel Trajectories of Long-Range Transport at Shanghai

1.INTRODUCTION Acid rain observations show that acid cloud water in Shanghai region appears not only

Parcels Delivered in 2019 to Reach 60 Billion

The State Post Bureau(SPB)predicts that the volume of parcels delivered this year will reach 60 billion,a 20 percent year-onyear growth.

Alarm on Invasion of Harmful Organisms Via Postal Parcels

In recent years,with the rapid development of cross-border e-commerce,international postal parcels have been increasing sharply,thus giving rise to a new way for alien biological invasion.According to the"animal and plant quarantine information platform"of the General Adminis-tration of Customs,postal inspection system intercepted a total of 4975 harmful organisms nationwide.There were 219 quarantine organisms,including 142 insects,27 weeds,12 nematodes,29 fungi,1 bacterium and 8 viruses.Insects accounted for 64.84%of the quarantine organisms,among which Hypothenemus hampei had the highest interception frequency.Up to 71.36%of those harmful organisms were from seedlings,yet47.31%organisms came from products exported from India.These analyses indicate the direction of future portal quarantine work and lay a theoret-ical foundation for better guarding the biological security.

A Uniform Parcel Delivery System Based on IoT

Nowadays, many different kinds of delivery companies transport their own kinds of parcels and offer their own services, which have caused a lot waste of resources. In addition, the volume of parcels in all cities that need to be delivered has been grown dramatically. To cope with these problems, a uniform parcel delivery system in a smart city which can offer service to all kinds of customers in the city including manufactures, department stores, restaurants, individual people and so forth was designed. This system uses IoT (Internet of Things) and RFID technology, combining computer network technology, wireless communication and cloud computing. With this system, the whole package delivery process including classification of packages, vehicle scheduling, path planning, transportation monitoring can be intellectualized as well as managed automatically, and the use of both material resources and manpower resources can be reduced accordingly.

Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution

Objective.Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity.Impact Statement.The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation,an important medical image analysis and neuroscientific problem.Introduction.The concept of“connectional fingerprint”has motivated many investigations on the connectivity-based cortical parcellation,especially with the technical advancement of diffusion imaging.Previous studies on multiple brain regions have been conducted with promising results.However,performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data.Methods.We propose the Spatial-graph Convolution Parcellation(SGCP)framework,a two-stage deep learning-based modeling for the graph representation brain imaging.In the first stage,SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network.In the second stage,SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region.Results.SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset.Performance comparisons between SGCP,traditional parcellation methods,and other deep learning-based methods show that SGCP can achieve superior performance in all the cases.Conclusion.Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements.

Summary of Animal and Plant Quarantine Objects in the Imported Parcels and X-ray Pattern Identification

The quantity of the imported parcels in China has increased rapidly,becoming an important means for criminals to hide the prohibited animal and plant quarantine objects.The x-ray applied to the port of post inspection can speed up inspection and better protect the ecological safety of China by mastering its method of judging animal and plant quarantine objects.

An end‐to‐end infant brain parcellation pipeline

Objective Accurate infant brain parcellation is crucial for understanding early brain development;however,it is challenging due to the inherent low tissue contrast,high noise,and severe partial volume effects in infant magnetic resonance images(MRIs).The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs.Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs.Specifically,in the global regions of interest(ROIs)localization stage,a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features,enabling an approximate localization of the ROIs across the whole brain.In the local ROIs refinement stage,leveraging the position priors from the first stage along with the raw MRIs,the boundaries of the ROIs are refined for a more accurate parcellation.Results We utilized the Dice ratio to evaluate the accuracy of parcellation results.Results on 263 subjects from National Database for Autism Research(NDAR),Baby Connectome Project(BCP)and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods.Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

微观地块服务设施可达性评价及路网优化影响分析

针对现有的服务设施可达性评价方法分析尺度大、数据精度低且无法依赖网络地图应用程序编程接口(Application Programming Interface,API)评价路网规划方案等问题,提出一种可用于规划方案评价的微观地块综合服务设施可达性评价方法.依据土地利用性质将研究区域划分为565个微观地块,利用实际居住建筑物空间分布测算各地块的人口分布;基于实际路网构建地铁-公交加权复合网络模型和小汽车网络模型,并设计通行时间的计算方法;利用改进潜力模型精确地计算各地块的医疗、教育、就业、商业和文化休闲5类设施的可达性,加权得到综合可达性.此外,考虑路网规划方案对交通网络的更新,以评价规划方案对可达性的具体影响.研究结果表明:可达性与服务设施及交通网络的空间分布呈现强相关性,公共交通网络的合理布局可有效缓解由服务设施和路网差异引起可达性分布差异;优化路网能有效提高可达性水平,但这种改善效果在不同区域间存在差异,设施密度较高的区域提高幅度较大.此评价方法可以为城市各类公共服务设施的合理配置、交通网络的规划与优化,以及社会公平性的增强提供新的视角和工具.

对流风暴大气不稳定机制研究的若干问题

大气不稳定是强对流天气发生的必要条件之一,具有复杂性。首先简要回顾了气块假设,给出了该假设的应用局限性,比如气块在对流风暴中的强上升运动必然会导致环境大气气压和涡度的变化等;然后梳理了大气的静力不稳定、对称不稳定以及其他多种类型不稳定的概念,重点总结了条件不稳定、湿绝对不稳定和条件对称不稳定的判据及其与对流风暴发生发展的关系,同时澄清了一些错误认识。判别条件不稳定最有效的方法是对气块作有限虚拟位移、使用对流有效位能(CAPE)来判别。CAPE和对流抑制能量的计算对抬升气块的温湿状况较为敏感,并需要进行虚温订正;最优CAPE值较地表CAPE具有更好的代表性。在强垂直风切变、低CAPE环境中,由于旋转导致的动力扰动气压梯度的加速作用对强对流风暴的发展至关重要;对流不稳定不一定对应于条件不稳定。条件对称不稳定的方便判别方法是使用饱和湿地转位势涡度,文中进一步总结了该不稳定所致的中尺度雨带特征。

一种自动求解宗地四至的方法

准确反映宗地四至关系是地籍测量的一项基本要求。为改变传统地籍内业整理过程烦琐、人工参与度大、容易出错等现实问题,本文从广东省“房地一体”农村宅基地确权发证数据库建设工作中宗地四至填写实际需要出发,在阿尔克·吉斯(ArcGIS)平台空间分析功能基础上,自主研究了一种求解宗地相互关系、判别并填写数据库四至的方法。该套方法经湛江市坡头区、清远市清新区“房地一体”不动产确权发证等实际项目应用,免除了人工填写四至工作,取得了很好的应用效果,可为今后批量求解地物四至提供借鉴参考。

Cloudera Manager扩展MariaDB服务的方法研究

大数据平台的优点在于能够快速处理超出单机能力的数据,平台中的数据仓库不仅具备存储功能,还兼具离线式的数据处理和分析能力。然而,数据仓库在服务响应方面效率不高,并且事务处理支持较弱,因此许多企业仍采用传统关系数据库提供查询和联机事务处理服务。Cloudera Manager管理的CDH可以动态扩展数据仓库,但不能部署关系数据库,为突破这局限性,以MariaDB为研究对象,并利用Cloudera Manager的扩展能力引入MariaDB parcel应用包和CSD管理包用来实现对其部署和管理。最终,经过实际验证,通过Cloudera Manager可视化动态部署和监控管理MariaDB服务是完全可行的,这为关系数据库服务的扩展奠定了基础。

客车代运与送提一体下的山区农村寄递网络优化

高效畅通的“最初—最后一公里”是农村寄递物流体系可持续运行的关键。针对山区农村寄递需求量小分散、客运班线空载率高的现实特征,本文构建以总成本最小为目标的客车代运与送提一体模式下的寄递网络优化模型,对中转点和服务站位置、代运车辆以及配送车辆路径进行决策。据此,采用有效不等式和热启动策略设计加强Benders分解(Benders Decomposition,BD)算法进行模型求解。通过四川省青川县某乡镇的实例分析验证了模型和算法的有效性。结果表明:客车代运与送提一体下的网络设计模式更具成本优势,可以带来至少4.91%的总成本节约,加强BD算法的求解时间较传统BD算法平均低66.06%。敏感性分析发现,总成本随着客车可用于装货容量的增加呈现阶梯性的降低趋势;总成本增加幅度随着覆盖半径的减小呈现先减后增的趋势。

国土空间规划背景下广东省村庄建设供需矛盾破解思路探讨

严控村庄建设用地新增是国土空间规划报批审查的重点内容,文章基于村庄建设用地“减量化”大趋势,以广东省遂溪县为例,分析了乡村建设面临的用地供需矛盾,提出了分类腾挪使用203范围“插花地”、建立乡村“指标+功能”留白机制、以乡村群为单元统筹土地资源三大路径破解乡村建设用地难题。本研究能够为乡镇级国土空间规划、村庄规划编制提供技术思路。