Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every year.The wheat rust diseases are identified either through experienced evaluators or computerassisted techniques.The experienced evaluators take time to identify the disease which is highly laborious and too costly.If wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield quality.To solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease identification.In the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation techniques.The augmented images are preprocessed by histogram equalization.As a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch extraction.Different layers of region extraction models construct a feature vector that is later passed to the CE-SVM model.As a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

A New Method to Locate the Region of Interest( ROI) of Palmprint

Biometric identification was a kind of identity recognition technology by making use of the human's unique physiological or behavioral characteristics,it provided a high reliability and stability way for the identification. Global threshold binarization palmprint image is used in this paper,and the bio-morphological methods are used to get the sensitive area of palmprint image's positioning point,so as to extract the region of interest. The palmprint collection is realized on the FPGA chip,and this kind of collection method uses the DSP Builder toolbox to realize visual programming in Matlab / Simulink and achieve fast modeling and development. The practice proves that this method is simple,flexible and its equipment is portable and fast.

Island instantaneous coastline extraction based on the characteristics of regional statistics of multispectral remote sensing image

This study adopted IKONOS remote sensing images and selected spectral characteristic areas, through regional pixel statistics and calculating weight coefficients of each band, processed the images with the spectral normalized method, which made the features of islands, land and water features more obviously in the images. On this basis, the OTUS was used to determine the optimal segmentation threshold, and the normalization image binarization was made, thus the island coastline was extracted. This method used the characteristic curve method to separate the land and water, obtained the binarization images and maintained the original edge effectively. The coastline that was extracted by Binary Morphology was continuous, reliable and high signal-to-noise ratio. The results showed that this method could extract the coastline fast, simply and effectively, which had the practical value.

A New Region-of-interest Coding Method to Control the Relative Quality of Progressive Decoded Images

Based on the ideas of controlling relative quality and rearranging bitplanes, a new ROI coding method for JPEG2000 was proposed, which shifts and rearranges bitplanes in units of bitplane groups. It can code arbitrary shaped ROI without shape coding, and reserve almost arbitrary percent of background information. It also can control the relative quality of progressive decoded images. In addition, it is easy to be implemented and has low computational cost.

Autonomous Lane Keeping Solution Based on Adaptive Gray Threshold and Region of Interest

Autonomous lane keeping is an important technology in intelligent transportation,which is used to avoid unnecessary traffic accidents caused by lane departure.To adapt different lighting environment,and make up ordinary Hough transform’s shortcomings of tardiness and poor immunity,we propose an improved algorithm by using adaptive gray threshold and setting Region of interest(ROI),to do the quick Hough transform for tracking lane line,implementing autonomous lane keeping. The dynamic adaptive threshold method can be suitable with different lighting conditions and quickly,accurately remove most of the information not relative to lane line.Meanwhile setting ROI can let the program only care about the specific region which can provide useful information and further reduce the processing data.And then on the basic of identification,we put forward some efficient innovation strategy about the control logic of straight state,curve state and the transition state.The experiment proves that this solution greatly raises efficiency.

A mathematical morphological approach for region of interest coding of microscopy image compression

A novel mathematical morphological approach for region of interest(ROI) automatic determination and JPEG2000-based coding of microscopy image compression is presented.The algorithm is very fast and requires lower computing power,which is particularly suitable for some irregular region-based cell microscopy images with poor qualities.Firstly,an active threshold-based method is discussed to create a rough mask of regions of interest(cells).And then some morphological operations are designed and applied to achieve the segmentation of cells.In addition,an extra morphological operation,dilation,is applied to create the final mask with some redundancies to avoid the"edge effect"after removing false cells.Finally,ROI and region of background(ROB) are obtained and encoded individually in different compression ratio flexibly based on the JPEG2000,which can adjust the quality between ROI and ROB without coding for ROI shape.The experimental results certify the effectiveness of the proposed algorithm,and compared with JPEG2000,the proposed algorithm has better performance in both subjective quality and objective quality at the same compression ratios.

A Method of Text Extremum Region Extraction Based on Joint-Channels

Natural scene recognition has important significance and value in the fields of image retrieval,autonomous navigation,human-computer interaction and industrial automation.Firstly,the natural scene image non-text content takes up relatively high proportion;secondly,the natural scene images have a cluttered background and complex lighting conditions,angle,font and color.Therefore,how to extract text extreme regions efficiently from complex and varied natural scene images plays an important role in natural scene image text recognition.In this paper,a Text extremum region Extraction algorithm based on Joint-Channels(TEJC)is proposed.On the one hand,it can solve the problem that the maximum stable extremum region(MSER)algorithm is only suitable for gray images and difficult to process color images.On the other hand,it solves the problem that the MSER algorithm has high complexity and low accuracy when extracting the most stable extreme region.In this paper,the proposed algorithm is tested and evaluated on the ICDAR data set.The experimental results show that the method has superiority.

Innovative Technologies for Large-Scale Water Production in Arid Regions: Strategies for Sustainable Development

Water scarcity in arid regions poses significant challenges to sustainable development and human well-being. This article explores both existing and innovative technologies and methods to produce large amounts of water to address these challenges effectively. Key approaches include atmospheric water generation, advanced desalination techniques, innovative water collection methods such as fog nets and dew harvesting, geothermal water extraction, and water recycling and reuse. Each method is evaluated for its feasibility with existing technology, potential time of implementation, required investments, and specific challenges. By leveraging these technologies and combining them into a multifaceted water management strategy, it is possible to enhance water security, support agricultural and industrial activities, and improve living conditions in arid regions. Collaborative efforts between governments, private sector entities, and research institutions are crucial to advancing these technologies and ensuring their sustainable implementation. The article provides a comprehensive overview of the current state of these technologies, their potential for large-scale application, and recommendations for future research and development.

The automatic height extraction of buildings from shadow region

Height extraction for buildings is a fundamental step of 3D scene reconstruction in many virtual reality applications. In this paper, we propose an automatic method to extract the height of buildings in high resolution satellite imagery based on the length of shadow. Taking into account the limitation of traditional algorithms, we make use of the boundary information of a building to facilitate detecting and matching the shadow regions with higher accuracy. Then, we introduce a shadow-cast model to correct the shadow location in our system. The experimental result shows that when extracting the height of buildings from complex urban regions, our method has better accuracy.

一种鲁棒的基于对抗结构的生物特征ROI提取方法

感兴趣区域(Region of interest,ROI)提取在生物特征识别中,常用于减少后续处理的计算消耗,提高识别模型的准确性,是生物识别系统中预处理的关键步骤.针对生物识别数据,提出了一种鲁棒的ROI提取方法.方法使用语义分割模型作为基础,通过增加全局感知模块,与分割模型形成对抗结构,为模型提供先验知识,补充全局视觉模式信息,解决了语义分割模型的末端收敛困难问题,提高了模型的鲁棒性和泛化能力.在传统二维(2D)指纹、人脸、三维(3D)指纹和指纹汗孔数据集中验证了方法的有效性.实验结果表明,相比于现有方法,所提出的ROI提取方法更具鲁棒性和泛化能力,精度最高.

基于动态ROI识别的智能车辆车道线检测

车道线检测技术是实现汽车无人驾驶的关键技术之一,能够帮助无人驾驶更快地实时处理从相机捕获的图像。近年来,研究人员在车道线检测的精度上取得了很大的进展,但在实际驾驶中,车辆可能会因有限的控制器算力而对车道线检测的速度和准确度产生影响。提出了一种动态识别感兴趣区域的方法,以Donkey car自动避障小车为主体进行实车试验,通过实时减少摄像头采集的图像中干扰信息,更加准确地识别车道线,在采集的图像数量不变的情况下,提高了卷积神经网络(convolutional neural networks,CNN)模型训练的速度和小车自动驾驶时车道线识别的准确度,进而减少了因车道线误判而存在的压线和驶离路面的情况。优化后的模型比原模型在训练时间方面缩短了约38.71%,在小车自动驾驶时间上缩短了约21.67%,同时小车在转弯处行驶速度更加均匀,左右摇摆幅度减小。结果表明,所提方法具有良好的检测效果和准确率。

Plane extraction for navigation of humanoid robot

In order to make the humanoid robot walk freely in complicated circumstance, the reliable capabilities for obtaining plane information from its surroundings are demanded. A system for extracting planes from data taken by stereo vision was presented, After the depth image was obtained, the pixels of each line were scanned and split into straight line segments. The neighbouring relation of line segments was kept in link structure. The groups of three line segments were selected as seed regions. A queue was maintained for storing seed regions, and then the plane region was expanded around the seed region. The process of region growing continued until the queue of seed regions was empty. After trimming, the edges of the planes became smooth. In the end, extracted planes were obtained. In the experiment, two models were used： pipe and stairs. Two planes in pipe mode/and six planes in stairs model were extracted exactly. The speed and precision of algorithm can satisfy the demands of humanoid robot＇s navigation.

A Road Extraction Method Based on Region Growing and Mathematical Morphology from Remote Sensing Images

Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottleneck which restricts economic development. In recent years, natural disasters frequently occur in China. Therefore, it is essential to extract road information to compute the degree of road damage for traffic emergency management. A road extraction method based on region growing and mathematical morphology from remote sensing images is proposed in this paper. According to the road features, the remote sensing image is preprocessed to separate road regions from non-road regions preliminarily. After image thresholding, region growing algorithm is used to extract connected regions. Then we sort connected regions by area to exclude the small regions which are probably non-road objects. Finally, the mathematical morphology algorithm is used to fill the holes inside the road regions. The experimental results show that the method proposed can effectively extract roads from remote sensing images. This research also has broad prospects in dealing with traffic emergency management by the government.

A NEW APPROACH TO DVC COMBINED WITH ROI EXTRACTION AND RESIDUAL VIDEO CODING FOR MULTIMEDIA SENSOR NETWORKS

The Wyner-Ziv distributed video coding scheme is characterized for its intraframe encoder and interframe decoder which can also approach the efficiency of an interframe encoder-decoder system. In Wyner-Ziv residual coding of video, the residual of a frame with respect to a reference frame is Wyner-Ziv encoded, which can reduces the input entropy and leads to a higher coding efficiency than directly encoding the original frame. In this paper, we propose a new approach of residual coding combined with Region Of Interest (ROI) extraction. Experimental results show that, the proposed scheme achieves better rate-distortion performance compared to conventional Wyner-Ziv coding scheme.

基于改进Faster RCNN的微操作空间目标检测算法

将Faster RCNN引入微操作系统的目标检测之中。针对微操作空间下待检测目标存在尺度变化和在显微镜放大倍数较小时,待检测目标尺度过小、特征不明显的问题,提出了一种基于改进Faster RCNN的微操作空间目标检测算法。使用在图像分类任务中性能优越的深度残差网络提取图像的特征。引入递归特征金字塔网络,对特征进行融合。改进区域建议网络的采样策略,对损失函数进行优化。实验结果表明:这种改进的Faster RCNN算法能有效解决由于目标尺度变化和目标尺度过小带来的问题。相比通用的目标检测算法,该算法的准确度更高,速度更快,具有实际应用价值。

基于CNN-OBIA的黄河源区水体提取及时空变化

准确识别水体信息是分析地表水时空动态变化的重要技术手段。针对目前各种长时序水体信息提取方法精度低的问题,基于Landsat遥感影像,选用1986~2022年5484景黄河源区遥感影像,分别运用卷积神经网络结合面向对象(CNN-OBIA)和多指数水体检测规则(MIWDR)两种方法提取了黄河源区的地表水体,并对两种方法的提取精度进行了对比分析。在此基础上,探究了1986~2022年黄河源区水体信息的时空变化特征,并对其主要气候因素进行相关分析。结果表明:①CNN-OBIA的总体精度和Kappa系数分别为96.78%和0.93,MIWDR的总体精度和Kappa系数分别为94.28%和0.88,总体而言,CNN-OBIA的提取精度高于MIWDR方法。CNN-OBIA的提取结果可以很好地保持水体边界完整性和有效去除山体阴影,可以较好地对细小河流进行提取。②研究区水体总面积呈现出先减少(1986~2001年)后增加(2001~2022年)的变化趋势。③相关性分析表明,降水和气温与水体面积的变化均表现出显著正相关。

远距离和遮挡下三维目标检测算法研究

针对现有三维目标检测算法对存在遮挡及距离较远目标检测效果差的问题,以基于点云的三维目标检测算法(3D object proposal generation and detection from point cloud,PointRCNN)为基础,对网络进行改进,提高三维目标检测精度。对区域生成网络(region proposal network,RPN)获取的提议区域(region of interest,ROI)体素化处理,同时构建不同尺度的区域金字塔来捕获更加广泛的兴趣点;加入点云Transformer模块来增强对网格中心点局部特征的学习;在网络中加入球查询半径预测模块,使得模型可以根据点云密度自适应调整球查询的范围。最后,对所提算法的有效性进行了试验验证,在KITTI数据集下对模型的性能进行评估测试,同时设计相应的消融试验验证模型中各模块的有效性。

基于能量函数基元提取的建筑物LOD2模型自动生成方法

近年来,随着实景三维中国和数字中国的快速发展,建筑物三维模型重建已成为智慧城市建设的重要环节。本文提出了一种全自动建筑物LOD2模型生成框架,旨在解决城市级实景三维场景中建筑物LOD2模型生成的难题。为了自动提取大场景建筑物对象,首先利用场景正射影像,通过图像分割获取建筑物的边界信息;然后使用改进的增强型平面区域分割方法提取高质量的分割平面,并利用混合线性模型对分割结果进行优化,能够更好地处理城市建筑物的局部破损和重建错误等问题。试验结果表明,本文算法生成的建筑物LOD2模型质量和效率更高,能够应对城市建筑模型的复杂情况,得到更稳健的重建结果。