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.

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 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.

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 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.

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.

Feature Extraction to Polar Image

Some algorithms of feature extraction in existing literature studied for image processing was the gray image with one-dimensional parameter. However, some feature points’ extraction for three-dimensional color of polar image, such as the color edge extraction, inflection points, and so on, was urgently to be solved a polar color problem. For achieving quickly and accurately the color feature extraction to polar image, this paper proposed a similar region of color algorithm. The algorithm was compared to polar image, and the effect to color extraction was also described by the combination of the proposed and existing algorithms. Moreover, this paper gave the comparison of the proposed algorithm and an existing classical algorithm to extraction of color feature. These researches in this paper provided a powerful tool for polar image classification, color feature segmentation, precise recognition, and so on.

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.

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.

An Efficient Mechanism for Product Data Extraction from E-Commerce Websites

A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular product.Websites are designed for human understanding and not for machines.Therefore,to make data machine-readable,it requires techniques to grab data from web pages.Researchers have addressed the problem using two approaches,i.e.,knowledge engineering and machine learning.State of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web page.Machine learning approaches use annotated pages to learn rules.These rules are used to extract data from unseen web pages.The structure of web documents is continuously evolving.Therefore,new techniques are needed to handle the emerging requirements of web data extraction.In this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of documents.The proposed technique detects Rich Data Region(RDR)using query and correlative words of the query.RDR is then divided into data records using style similarity.Noisy elements are removed using a Common Tag Sequence(CTS)and formatting entropy.The system is implemented using JAVA and runs on the dataset of real-world working websites.The effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.

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.

图像亮度特征对ROI提取的影响

提出一个基于改进的Itti-Koch模型的感兴趣区域(Region of interest,ROI)提取算法,同时针对图像亮度特征对ROI提取的影响问题,从2个方面进行分析研究:一是根据不同亮度权重下提取的ROI,分析亮度特征对ROI提取的影响程度;二是对眼动数据提取图像的ROI和基于改进的Itti-Koch模型提取的图像ROI进行区域评价,计算两者之间的点对点区域相似度和位置区域相似度。研究结果表明:当亮度特征和颜色特征同时影响图像ROI提取时,亮度特征所占权重不宜超过0.5。

一种新的基于区域增长的ROI分割算法

为克服现有算法带来边缘定位不精确和人工参与太多等缺点,提出了一种新的基于区域增长的适合医学图像中ROI的分割算法。该算法先利用改进的Canny边缘算子进行边缘粗检测,再利用给出的灰度和纹理等信息进行区域增长,最终得到分割图像。为了更好地进行区域增长,新算法通过对ROI中像素的灰度和纹理进行分析,给出结合点向量运算和灰度判断的增长准则。实验结果表明,该方法能对医学图像中复杂区域或畸形区域进行分割,具有很好的鲁棒性与实用性。

一种基于ROI区域生长的医学图像压缩方法

为了克服传统基于ROI的压缩方法提高不了含有狭长的不规则ROI图像的压缩比等缺点,提出一种基于ROI区域生长的医学图像压缩方法。该方法使用区域生长的方法,将医学图像的ROI分割提取后,对ROI和非感兴趣区域(RONI)采用无损和有损相结合的方法,较好地解决了医学图像的高压缩比和高质量的矛盾,缩短了压缩时间。同时该方法还提供了良好的人机交互方式,便于操作,并能够很好地适用于其他场合的压缩。

面向ROI编码的立体图像比特分配策略分析

为了提高基于感兴趣区域（ROI）的立体图像编码的效率，从人眼视觉感知的角度提出了一种探寻合理的立体图像比特分配策略的方法。该方法设计了两个主观实验，其中实验I对各种比特分配策略下的失真立体图像进行视觉质量评价，而实验II在实验I的基础上对所有失真立体图像进行两两对比实验。实验结果表明：合理的比特分配策略对基于ROI的立体图像编码至关重要，且与图像内容无关，但与立体图像质量档次有关；在立体图像ROI采用量化参数（QP）为22、27、32、37的4种不同质量档次编码时，对应的合理分配策略下非ROI与ROI之间QP差异值分别约为9、6、3、3；立体图像ROI与非ROI比特分配策略的确定，能够在保证立体图像整体主观质量不变的同时有效地提高立体图像的编码效率。

ROI的海洋监视卫星遥感图像压缩算法

针对海洋监视卫星遥感图像的特点，提出了一种基于感兴趣区域（ROI）的自适应海洋遥感图像近无损压缩算法。对图像进行了形态Harr小波最大提升后，在一个分辨率低的高频子带中利用阈值和八邻域连通分析方法检测出目标，使用外接矩形与环面的交集来描述ROI，其他分辨率高频子带的ROI通过Mosaic放大得到。高频子带中的ROI采用Rice无损熵编码方法，非ROI进行比特平面编码。低频子带采用DPCM和Rice结合的无损编码方法。实验结果表明，该算法能有效地划分ROI，且没有明显的R01分割痕迹。在中低比特率时，本文算法的峰值信噪比（PSNR）比JPEG2000的高2～5dB，在高比特率时，两者的PSNR均在45dB以上。本文算法计算复杂度低，易于硬件实现，且具有自适应性和数据包独立的优点，适用于海洋监视卫星遥感图像的近无损压缩。

基于ROI提取的多目标图像水平集分割

针对包含多个目标或目标灰度与背景灰度接近的图像分割问题,借鉴人类视觉系统的关注机制和多分辨性,提出一种多尺度框架下基于感兴趣区域提取的图像水平集分割方法。对原图像小波变换的低频分量图应用显著性特征提取出感兴趣区域,将图像域分成多个感兴趣子区域和一个背景子区域,在各目标子区域中,采用C-V模型方法进行曲线演化,并对各子区域分割结果进行合成。仿真结果标明,该算法能有效分割多目标图像。

基于ROI多特征和相关反馈的图像检索算法

本文提出了一种基于感兴趣区域(ROI)综合多特征和相关反馈的图像检索算法。在对图像进行四叉树分解的基础上,由用户选择感兴趣区域,综合颜色、纹理和形状三种底层特征进行多层感兴趣区域检索;由相关反馈技术体现用户感知的主观性,通过调整感兴趣区域、特征间、特征内的权重来提高查准率。给出了改进的CBIR通用模型和特征权重模型。实现了一个图像检索原型系统,将不同实验结果进行了比较和分析,实验结果表明,该文提出的方法具有良好的检索效果。