Material Master、Enhancement for Material Master

本文介绍了多个针对物料主数据的业务增强插件(BAdI)及其功能,包括物料存在性的检查、归档增强、特殊字段选择等。通过这些插件可以实现对物料主数据的有效管理和使用。

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Enhancement

MGA00001 Material Master (Industry): Checks and Enhancements

MGA00002 Material Master (Industry): Number Assignment

MGA00003 Material Master (Industry and Retail): Number Display

Business Add-in

CDT_CHECK_MATERIAL Checks for Existence of a Material in a CDT

BADI_MM_MATNR Modification-Free Archiving Enhancement of MM_MATNR

BADI_MAT_F_SPEC_SEL BAdI for Material Special Field Selection

BADI_MATNR_CHECK_PVS Check Material for Use in iPPE

BADI_MATERIAL_REF Addition of customer-defined default data for material

BADI_MATERIAL_OD Integration of New Objects in Material or Article Master

BADI_MATERIAL_CHECK Enhanced Checks for Material Master Tables

BADI_GTIN_VARIANT User Exit for Customer-Specific GTIN Variant Check

BADI_EAN_SYSTEMATIC BAdI for Internal Control of EAN Logic

WRF_DISCONT_PARAMS_I BAdI: Parameters in Fashion Discontinuation

WRF_DISCONT_FACT_E BAdI: Follow-Up Actions in Discontinuation

WRF_DISCONT_CHECKS_I BAdI: Scope of Check in Material Reorganization

MG_MASS_NEWSEG User-Specific Fields & Segments in Mass Maintenance

MATGRP_SKU_UPD BAdI for Article Hierarchy Connection

Object detection in remote sensing images is a challenging task due to the complex backgrounds, diverse object shapes and sizes, and varying imaging conditions. To address these challenges, fine-grained feature enhancement can be employed to improve object detection accuracy. Fine-grained feature enhancement is a technique that extracts and enhances features at multiple scales and resolutions to capture fine details of objects. This technique includes two main steps: feature extraction and feature enhancement. In the feature extraction step, convolutional neural networks (CNNs) are used to extract features from the input image. The extracted features are then fed into a feature enhancement module, which enhances the features by incorporating contextual information and fine-grained details. The feature enhancement module employs a multi-scale feature fusion technique to combine features at different scales and resolutions. This technique helps to capture fine details of objects and improve the accuracy of object detection. To evaluate the effectiveness of fine-grained feature enhancement for object detection in remote sensing images, experiments were conducted on two datasets: the NWPU-RESISC45 dataset and the DOTA dataset. The experimental results demonstrate that fine-grained feature enhancement can significantly improve the accuracy of object detection in remote sensing images. The proposed method outperforms state-of-the-art object detection methods on both datasets. In conclusion, fine-grained feature enhancement is an effective technique to improve the accuracy of object detection in remote sensing images. This technique can be applied to a wide range of applications, such as urban planning, disaster management, and environmental monitoring.
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