- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) - Riga, Latvia (2019.10.7-2019.10.9)] 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) - Flicker Characteristics of a Multi-Channel Current-Controlled PWM Dimming Method of LED Lightings
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： spectral and spatial information,Hyperspectral image,low rank (LR),restoration,nonlocal means
更新于2025-09-19 17:13:59
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Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information
摘要： Landcover classifications have large uncertainty related to the heterogeneity of similar objects and complex spatial correlations in satellite images, making it difficult to obtain ideal classification results using traditional classification methods. Therefore, to address the uncertainty in landcover classifications based on remotely sensed information, we propose a novel fuzzy c-means algorithm, which integrates adaptive interval-valued modelling and spatial information. It dynamically adjusts the interval width according to the fuzzy degree of the target membership without pre-setting any parameters, controls the fuzziness of the target, and mines the inherent distribution of the data. Furthermore, reliability-based spatial correlation modelling is used to describe the spatial relationship of the target and to improve both robustness and accuracy of the algorithm. Experimental data consisting of SPOT5 (10-m spatial resolution) or Thematic Mapper (30-m spatial resolution) satellite data for three case study areas in China are used to test this algorithm. Compared with other state-of-the-art fuzzy classification methods, our algorithm markedly improved the ground-object separability. Moreover, it balanced improvement of pixel separability and suppression of heterogeneity of intra-class objects, producing more compact landcover areas and clearer boundaries between classes.
关键词： satellite images,spatial information,adaptive interval fuzzy c-means algorithm,Landcover classification
更新于2025-09-11 14:15:04
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Polarisation of Light || PHENOMENA PRODUCED BY MECHANICAL MEANS—UNANNEALED GLASS
摘要： The chapter discusses the phenomena produced by mechanical means on unannealed glass, focusing on how mechanical strain can impart a structural character to glass analogous to that of a crystal, affecting the motion of the ether within it and exhibiting chromatic effects with polarised light. It describes experiments involving straining a rectangular bar of ordinary glass and observing the light passing through it, as well as the effects of squeezing a thick square plate of glass in a vice. The chapter also explores the impact of molecular forces due to heat and cooling on glass, and the permanent, splendid effects produced by unannealed glass that has been rapidly and unequally cooled.
关键词： crystal,polarisation,light,molecular forces,unannealed glass,mechanical means,pressure,heat,strain,chromatic effects,cooling
更新于2025-09-11 14:15:04
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Identification of tea varieties by mid‐infrared diffuse reflectance spectroscopy coupled with a possibilistic fuzzy c‐means clustering with a fuzzy covariance matrix
摘要： Mid-infrared diffuse reflectance spectroscopy was used to rapidly and nondestructively identify tea varieties together with the proposed possibilistic fuzzy c-means (PFCM) clustering with a fuzzy covariance matrix. The mid-infrared diffuse reflectance spectra of 96 tea samples with three different varieties (Emeishan Maofeng, Level 1, and Level 6 Leshan trimeresurus) were acquired using the FTIR-7600 infrared spectrometer. First, multiplicative scatter correction was implemented to pretreat the spectral data. Second, principal component analysis was employed to compress the mid-infrared diffuse reflectance spectral data after preprocessing. Third, linear discriminant analysis was utilized for extracting the identification information required by the fuzzy clustering algorithms. Ultimately, the fuzzy c-means (FCM) clustering, the allied fuzzy c-means (AFCM) clustering, the PFCM clustering, and the PFCM clustering with a fuzzy covariance matrix were used to cluster the processed spectral data, respectively. The highest identification accuracy of the PFCM clustering with a fuzzy covariance matrix reached at 100% compared with those of FCM (96.7%), AFCM (94.9%), PFCM (96.3%), and partial least squares discrimination analysis (PLS-DA) algorithm (33.3%). It is sufficiently demonstrated that the mid-infrared diffuse reflectance spectroscopy coupled with the PFCM clustering with a fuzzy covariance matrix was a valid method for identifying tea varieties.
关键词： possibilistic fuzzy c-means clustering,tea varieties,Mid-infrared diffuse reflectance spectroscopy,fuzzy covariance matrix,nondestructive detection
更新于2025-09-11 14:15:04
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[IEEE 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Machine Learning and Cybernetics (ICMLC) - Image Segmentation Algorithm Based On Clustering
摘要： Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.
关键词： Fuzzy C-means algorithm,Clustering,Image segmentation,Mean shift algorithm
更新于2025-09-10 09:29:36
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[IEEE 2018 International Russian Automation Conference (RusAutoCon) - Sochi (2018.9.9-2018.9.16)] 2018 International Russian Automation Conference (RusAutoCon) - Object Hierarchy in a Digital Image
摘要： The paper describes a model of binary hierarchical clustering of image pixels for object detection. In the model, a hierarchical sequence (a hierarchy) of pixel clusters is obtained adaptively to an image by iterative merging of pixel sets. Clustering of pixels depending on the number of clusters is given by a hierarchy of piecewise-constant approximations of the image and is described by a convex sequence of corresponding values of the total quadratic error, which is minimized for a given number of clusters. Due to the convexity property, the pixel clusters and their colors in the image are ordered by the absolute value of the increment of the total squared error accompanied by the dividing of cluster in two parts. For the hierarchy of pixel clusters, the problem of unambiguous assignment of image points to detected objects is formalized. In this case, the output of object detection is a sequence of object associations that incrementally reveal or disappear on a certain background. Objects are detected in accordance with the threshold value of the number of pixels in the cluster, or the threshold for the increment of the total squared error, or by other pixel cluster attributes that have a sense of a quantitative measure. The hierarchy of pixel clusters and the hierarchy of object associations are encoded with "pixel rating" stereo pair and "object rating" stereo pair. The pilot experimental results are demonstrated.
关键词： digital image,minimization,Ward’s pixel clustering,piecewise constant approximations,standard deviation,K-means method
更新于2025-09-10 09:29:36
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LASSO & LSTM Integrated Temporal Model for Short-term Solar Intensity Forecasting
摘要： As a special form of the Internet of Things, Smart Grid is an internet of both power and information, in which energy management is critical for making the best use of the power from renewable energy resources such as solar and wind, while efficient energy management is hinged upon precise forecasting of power generation from renewable energy resources. In this paper, we propose a novel least absolute shrinkage and selection operator (LASSO) and long short term memory (LSTM) integrated forecasting model for precise short-term prediction of solar intensity based on meteorological data. It is a fusion of a basic time series model, data clustering, a statistical model and machine learning. The proposed scheme first clusters data using k-means++. For each cluster, a distinctive forecasting model is then constructed by applying LSTM, which learns the non-linear relationships, and LASSO, which captures the linear relationship within the data. Simulation results with open-source datasets demonstrate the effectiveness and accuracy of the proposed model in short-term forecasting of solar intensity.
关键词： Internet of Things (IoT),Least absolute shrinkage and selection operator (LASSO),Short-term solar power forecasting,Long short term memory (LSTM),K-means++
更新于2025-09-10 09:29:36
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Location Ambiguity Resolution and Tracking Method of Human Targets in Wireless Infrared Sensor Network
摘要： Human tracking has attracted extensive attention by using low-cost pyroelectric infrared sensor network in recent years. This paper presents a location ambiguity resolution and tracking method for human targets in wireless, distributed and binary infrared sensor network. The tracking system can detect the human targets in the detection space, and activate the sensor detection lines dynamically. A bearing-crossing location method is designed. The intersections of all activated detection lines are called primary measurement points for human location, and some of them are false measurement points. The ambiguity of this bearing-crossing location method is discussed and a two-level bearing-crossing algorithm is proposed based on quartic K-means clustering and joint cost function. For the first level, an anti-logic algorithm is designed to get the initial effective measurement points, then these points are assigned to different targets using K-means clustering. For the second level, the final effective points are obtained by using a special joint cost function, and they are assigned to different targets using K-means clustering once again to get the final locating results. The cost value is used as a weight to adjust the covariance parameter in Kalman filter for target tracking as well. The experimental results show that the average tracking error of human targets is less than 0.8 m in a 10 m×10 m space, which verify the proposed location ambiguity resolution and tracking method.
关键词： Wireless Infrared sensor network,Cost function,Multiple human tracking,Binary pyroelectric infrared sensor network,Location ambiguity,Bearing-crossing location,Quadratic K-means clustering
更新于2025-09-10 09:29:36
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[IEEE 2018 International CET Conference on Control, Communication, and Computing (IC4) - Thiruvananthapuram, India (2018.7.5-2018.7.7)] 2018 International CET Conference on Control, Communication, and Computing (IC4) - Reversible and Secure False Color Based Image Privacy Protection with Color Palette Generation
摘要： In many areas, visual privacy needs the protection, especially in video surveillance. Video surveillance is considered as an important solution for the security and safety issues in public places, since it monitors behaviour, activities, or other changing information. Its usage is increased in today’s world which affects individual’s privacy. There comes the need of visual privacy protection in video surveillance. Local servers can not store and analyze this large amount of data from the surveillance camera. So usage of cloud servers can solve this problem. But there is a chance to acquire these data by unauthorized parties. Different schemes for data hiding are present, which reversibly encrypts this data. But such systems need to do the detection of sensitive regions either by using a computer vision module or manually which increases complexity and reduces reliability. So two fully reversible privacy protection schemes for images using false coloring can be used. First scheme is more generic i.e., it is ?exible with other privacy protection schemes and the next scheme is fully based on false colors. These schemes are independent of region-of-interest (ROI) detection and can be applied to the whole image. So the user can escape from the complex ROI detection. A color palette generation method is also added with these schemes which increases the security.
关键词： k-means clustering,scrambling,pixelation,ROI,blurring,privacy protection,false coloring
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Tabular K-Means Clustering on Remote Sensing Images
摘要： This research develops a Tabular K-means clustering approach that derives a discriminant look-up table (LUT) from the Voronoi diagram of initial K peaks automatically selected from the scatter plot of the top two principal components of the input images. Numerical experiments in clustering 7-band Landsat TM images into specified number of spectral clusters are illustrated for the advantages in convergence and computational efficiency of the proposed tabular approach against traditional approach in K-means clustering.
关键词： Voronoi diagram,peak detection,principal component transformation,K-means,clustering
更新于2025-09-10 09:29:36