Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component ana...
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Noise level is an important parameter for image denoising in many image-processing applications. We propose a noise estimation algorithm based on pixel-level low-rank, low-texture subblocks and principal component analysis for white Gaussian noise. First, an adaptive clustering algorithm, based on a dichotomy merge, adaptive pixel-level low-rank matrix construction method and a gradient covariance low-texture subblock selection method, is proposed to construct a pixel-level low-rank, low-texture subblock matrix. The adaptive clustering algorithm can improve the low-rank property of the constructed matrix and reduce the content of the image information in the eigenvalues of the matrix. Then, an eigenvalue selection method is proposed to eliminate matrix eigenvalues representing the image to avoid an inaccurate estimation of the noise level caused by using the minimum eigenvalue. The experimental results show that, compared with existing state-of-the-art methods, our proposed algorithm has, in most cases, the highest accuracy and robustness of noise level estimation for various scenarios with different noise levels, especially when the noise is high.
With the rapid advancement of renewable energy, the fault detection of photovoltaic modules has become a key link to ensure their efficient operation. The study first utilizes remote sensing technology to obtain high-...
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With the rapid advancement of renewable energy, the fault detection of photovoltaic modules has become a key link to ensure their efficient operation. The study first utilizes remote sensing technology to obtain high-resolution images of photovoltaic power plants, and then uses the Deeplabv3+model to segment the images and identify faulty components. Combining remote sensing technology with Deeplabv3+ model, fast and accurate photovoltaic module fault detection can be achieved. The research results indicated that bilateral filtering and gamma filtering algorithms showed superior performance in testing, with the highest indicator evaluation results. Meanwhile, the structural similarity index value was also very close to 1. The error of the improved K-means was mainly concentrated between 0.000 and 0.014, while the traditional K-means was mainly distributed between 0.022 and 0.051. The statistical test results showed that the improved K-means algorithm was significantly better than the traditional K-means in clustering accuracy, and its average error was only 0.008, which was much lower than the 0.035 of the traditional K-means. The bilateral filtering showed the highest accuracy, with a classification result of 88%. This method can detect faults in photovoltaic modules, which has significant advantages over traditional methods. It provides a new and efficient method for photovoltaic module fault detection, which helps to optimize the operational efficiency and reliability of photovoltaic power plants and promote the sustainable development of renewable energy.
According to the characteristic of hybrid electric bulldozer operating under various working conditions, clustering algorithm is used to monitor working conditions of hybrid electric bulldozer. In this study, firstly ...
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According to the characteristic of hybrid electric bulldozer operating under various working conditions, clustering algorithm is used to monitor working conditions of hybrid electric bulldozer. In this study, firstly -the pound hardware and software of remote monitoring system is developed and A large number of historical data are collected and stored in the database. Secondly, clustering algorithm is improved by using strong robustness of ant colony algorithm. Then a new definition of outlier data mining based on ant colony clustering is put forward by combining clustering analysis and some parameters of ant colony algorithm such as p(ij)(t), according to characteristics of the data point number and distance between data point and center point in clusters. In the end, the Outlier data mining is successfully realized by program code and a large amount of historical data of hybrid electric bulldozer are analyzed. Using the proposed algorithm in this paper, the outlier abnormal cluster can be easily found and realize early warning and operation optimization of hybrid electric bulldozer.
作者：
Yu, WenpingZhou, WeiWang, TingXiao, JieyunPeng, YaoLi, HaoranLi, YuechenChinese Acad Agr Sci
Inst Agr Resources & Reg Planning State Key Lab Efficient Utilizat Arid & Semiarid A Beijing 100081 Peoples R China Southwest Univ
Chongqing Engn Res Ctr Remote Sensing Big Data App Sch Geog Sci Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R Chongqing 400715 Peoples R China Chinese Acad Sci
Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Minist Nat Resources
Topog Survey Team 6 Chengdu 610500 Peoples R China
Soil organic carbon (SOC) is generally thought to act as a carbon sink;however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simula...
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Soil organic carbon (SOC) is generally thought to act as a carbon sink;however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm;then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55;RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47;RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44;RMSE = 4.35);although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%;however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy.
To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division...
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To address the problem of low overall machining efficiency of free-form surfaces and difficulty in ensuring machining quality, this paper proposes a MATLAB-based free-form surface division method. The surface division is divided into two stages: Partition area identification and area boundary determination. In the first stage, the free-form surface is roughly divided into convex, concave, and saddle regions according to the curvature of the surface, and then the regions are subdivided based on the fuzzy c-means clustering algorithm. In the second stage, according to the clustering results, the Voronoi diagram algorithm is used to finally determine the boundary of the surface patch. We used NURBS to describe free-form surfaces and edit a set of MATLAB programs to realize the division of surfaces. The proposed method can easily and quickly divide the surface area, and the simulation results show that the proposed method can shorten machining time by 36% compared with the traditional machining method. It is proved that the method is practical and can effectively improve the machining efficiency and quality of complex surfaces.
In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS...
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In high-traffic port areas, vessel traffic-management systems (VTMS) are essential for managing ship movements and preventing collisions. However, inaccuracies and omissions in the Automatic Identification System (AIS), along with frequent false tracks generated by radar false alarms in complex environments, can compromise VTMS stability. To address the challenges of establishing consistent navigation and improving trajectory quality, this study introduces a novel method to directly identify AIS-matched trajectories from radar plots. This approach treats radar points as probability clouds, generating a multi-dimensional information layer by stacking these clouds after differential transformations based on AIS data. The resulting layer undergoes filtering and clustering to extract point sets that align with AIS data, effectively isolating matching trajectories. The algorithm, validated with simulated data, rapidly identifies target trajectories amid extensive interference without requiring strict parameter adjustments. In measured data, the algorithm rapidly provides matching trajectories, although further human judgment is still required due to the potential absence of true values in measured data.
The increasing penetration of renewable energy resources in the distribution network has posed great uncertainties and challenges for the system security operation. To model various uncertain factors like the wholesal...
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The increasing penetration of renewable energy resources in the distribution network has posed great uncertainties and challenges for the system security operation. To model various uncertain factors like the wholesale market price and renewable energy generation in the active distribution network (ADN), a similarity measurement method considering the amplitude, volatility and variation trend is proposed. The Latin hypercube sampling method and Graph Pyramid clustering algorithm are adopted to obtain the comprehensive typical scenario set. Furthermore, this study proposes a scenario-based stochastic day-ahead optimal economic dispatch approach based on typical scenario set. The energy trading between the distribution system and the wholesale energy market, various distributed generators, network topology and power flow model are jointly formulated in the proposed operation model. The effectiveness and scalability of the proposed approach are verified using the IEEE 33-bus system. Numerical simulation results under different implementation scenarios indicate that the proposed approach offers a high computational efficiency and promotes the security and economy of the distribution system operation, which has a promising industrial application value.
The probabilistic linguistic term set is a powerful tool to express and characterize people's cognitive complex information and thus has obtained a great development in the last several years. To better use the pr...
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The probabilistic linguistic term set is a powerful tool to express and characterize people's cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information measures such as the distance measure, similarity measure, entropy measure and correlation measure should be defined. However, as an important kind of information measure, the inclusion measure has not been defined by scholars. This study aims to propose the inclusion measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and entropy measures of probabilistic linguistic term sets to construct a unified framework of information measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, entropy and inclusion measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China. (C) 2019 Elsevier B.V. All rights reserved.
Data dissemination in vehicular ad hoc network (VANET) is emerging as a critical area of research. One of the challenges posed by this domain is the reliability of connection, which depends on many parameters, such as...
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Data dissemination in vehicular ad hoc network (VANET) is emerging as a critical area of research. One of the challenges posed by this domain is the reliability of connection, which depends on many parameters, such as the bandwidth consumption, transmission delay, and data quality of service (QoS). Dissemination of emergency messages is very critical since the network topology is changing frequently and rapidly, which leads to data loss. So, it is necessary to develop new protocols and enhance dissemination schemes in VANET to avoid more emergencies and hazards cases. In this regard, we have proposed a new strategy, which consists of data handling before dissemination process as the first step of our scheme. In this phase, the original message is optimized in order to reduce the number of exchanged packets. The second part of this proposition consists of constructing fast and stable clusters to improve the message delivery time and to procure efficient bandwidth consumption. This approach is based on a Fitness function, which takes into account different parameters such as the transmission period, the connectivity degree, the relative velocity, and the link lifetime validity. Since exchanging data in VANET is an important process, routing phase is proposed to perform data exchange among clusters. It is based on a rapid and real-time heuristic (real-time adaptive A* [RTAA*]). To evaluate the reliability of the proposed approach, an urban scenario is used with different simulation parameters. The simulation results show that our proposed approach presents a better stability and efficiency performance compared with the discussed approaches. The proposed approach improves the performance of cluster duration (5% - 25%), delivery rate (2% - 8%), and the overhead (5% - 35%) on average compared with the discussed approaches.
Combining services has become a major challenge in the use of service-based technology in smart building. The composition of services has numerous problems, specifically the quality of service (QoS), becoming increasi...
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Combining services has become a major challenge in the use of service-based technology in smart building. The composition of services has numerous problems, specifically the quality of service (QoS), becoming increasingly difficult as dynamic environments change. The other concern is selecting services with a higher level of reliability based on trust value. A number of metaheuristic algorithms are employed to reduce the NP-hard complexity of service composition. Thus, the study adopts a hybrid gray wolf with cuckoo search optimization (HGWCSO) algorithm to achieve its objectives. The trust challenge is additionally addressed by using a trust-driven clustering algorithm using deep convolutional neural network (CNN) and k-means. The synergy between k-means and CNNs can result in improved clustering accuracy and better representation of underlying patterns in the data. According to the outcomes, the suggested algorithm is more effective in small-scale problems than the gray wolf optimization and particle swarm optimization algorithms. As the search space is clustered and reduced, response times are enhanced;it is also easier to select trusted services. The outcomes on the large-scale digital twin of smart building indicate that the suggested approach outperforms the average outcomes of similar studies in terms of computational time.
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