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A detection model for dust deposition on photovoltaic (PV) panels based on light transmittance estimation

Energy 2025年 322卷

作者： Chen, Linhong Fan, Siyuan Sun, Shengyao Cao, Shengxian Sun, Tianyi Liu, Peng Gao, Han Zhang, Yanhui Ding, Wei School of Automation Engineering Northeast Electric Power University Jilin 132012 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Dust deposition on photovoltaic (PV) panels significantly reduces light transmittance and power conversion efficiency. Therefore, real-time dust detection systems are crucial for proactive cleaning and maintenance to improve light absorption and the operational efficiency of PV systems. This paper developed an end-to-end PV dust detection model, DVNET, based on light transmittance estimation. The model quantifies the dust density on PV panels using image processing to estimate light transmittance and determine optimal cleaning strategies. The DVNET architecture captures the mapping relationship between light transmittance and dust density. The model calculates the light transmittance of dusty images, enabling a quantitative assessment of dust deposition. Images are acquired and preprocessed to eliminate noise and irrelevant information and obtain optimal model training conditions. Experiments are conducted to evaluate model performance. The results indicate that the proposed model accurately estimates light transmittance using images of PV panels and generates transmittance maps to visualize the dust distribution on the panel surfaces. DVNET outperforms five benchmark models, achieving the lowest mean square error (MSE) of 0.00044. The performances of the squeeze-and-excitation block (SEBlock) module, the convolutional block attention module (CBAM), and the multiply-add-combine (MAC) attention mechanism are evaluated. The experimentally determined relative error (RE) is below 0.03 for uniform and non-uniform transmittance. The observed and predicted correlations between dust deposition and transmittance are highly similar for PV panels with different dust densities, demonstrating the model's applicability in real-world scenarios. The experiment demonstrated that the DVNET model exhibits low computational resource consumption, fast training time, as well as remarkable scalability and computational performance on large-scale datasets. © 2025 Elsevier Ltd

关键词： Dust deposition density Image processing PV panels Transmittance estimation

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Constraint Boundary Wandering Framework: Enhancing Constrained Optimization with Deep Neural Networks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wu, Shuang Chen, Shixiang Shen, Li Zhang, Lefei Tao, Dacheng Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China University of Science and Technology of China Key Laboratory of the Ministry of Education for Mathematical Foundations and Applications of Digital Technology School of Mathematical Sciences China Shenzhen Campus of Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Constrained optimization problems are pervasive in various fields, and while conventional techniques offer solutions, they often struggle with scalability. Leveraging the power of deep neural networks (DNNs) in optimization, we present a novel learning-based approach, the Constraint Boundary Wandering Framework (CBWF), to address these challenges. Our contributions include introducing a boundary wandering strategy inspired by the active-set method, enhancing equality constraint feasibility, and treating the Lipschitz constant as a learnable parameter. Additionally, we evaluate the regularization term, illustrating that the nonsmooth L2 norm yields superior results. Extensive testing on synthetic datasets and the ACOPT dataset demonstrates CBWF's superiority, outperforming existing deep learning-based solvers in terms of both objective and constraint loss. © 1979-2012 IEEE.

关键词： Constrained optimization

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OpenLS-DGF: An Adaptive Open-Source Dataset Generation Framework for Machine Learning Tasks in Logic Synthesis

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Ni, Liwei Wang, Rui Liu, Miao Meng, Xingyu Lin, Xiaoze Liu, Junfeng Luo, Guojie Chu, Zhufei Qian, Weikang Yang, Xiaoyan Xie, Biwei Li, Xingquan Li, Huawei Chinese Academy of Sciences State Key Lab of Processors Institute of Computing Technology Beijing100190 China Pengcheng Laboratory Shenzhen518055 China University of Chinese Academy of Sciences Beijing101408 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing100049 China Peking University School of Computer Science Center for Energy-Efficient Computing and Applications Beijing100871 China Ninbo University Faculty of Electrical Engineering and Computer Science Ninbo315211 China Shanghai Jiao Tong University University of Michigan-Shanghai Jiao Tong University Joint Institute MoE Key Laboratory of Artificial Intelligence Shanghai200240 China Hangzhou Dianzi University School of Electronics and Information Engineering Hangzhou311121 China

This paper introduces OpenLS-DGF, an adaptive logic synthesis dataset generation framework, to enhance machine learning (ML) applications within the logic synthesis process. Previous dataset generation flows were tailored for specific tasks or lacked integrated ML capabilities. While OpenLS-DGF supports various machine learning tasks by encapsulating the three fundamental steps of logic synthesis: Boolean representation, logic optimization, and technology mapping. It preserves the original information in both Verilog and ML-friendly GraphML formats. The Verilog files offer semi-customizable capabilities, enabling researchers to insert additional steps and incrementally refine the generated dataset. Furthermore, OpenLS-DGF includes an adaptive circuit engine that facilitates the final dataset management and downstream tasks. The generated OpenLS-D-v1 dataset comprises 46 combinational designs from established benchmarks, totaling over 966,000 Boolean circuits. OpenLS-D-v1 supports integrating new data features, making it more versatile for new tasks. This paper demonstrates the versatility of OpenLS-D-v1 through four distinct downstream tasks: circuit classification, circuit ranking, quality of results (QoR) prediction, and probability prediction. Each task is chosen to represent essential steps of logic synthesis, and the experimental results show the generated dataset from OpenLSDGF achieves prominent diversity and applicability. The source code and datasets are available at https://***/Logic-Factory/ACE/blob/master/OpenLS-DGF. © 1982-2012 IEEE.

关键词： Information management

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Semi-Supervised Learning in Large Scale Text Categorization

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Journal of Shanghai Jiaotong university(Science) 2017年第3期22卷 291-302页

作者：许泽文李建强刘博毕敬李蓉毛睿 School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for Io T Software and Systems Beijing University of Technology Guangdong Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen Key Laboratory of Service Computing and Applications Shenzhen University

The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately because of its huge number. Automatic text classification technology based on machine learning can classify a large number of natural language documents into the corresponding subject categories according to its correct semantics. It is helpful to grasp the text information directly. By learning from a set of hand-labeled documents,we obtain the traditional supervised classifier for text categorization(TC). However, labeling all data by human is labor intensive and time consuming. To solve this problem, some scholars proposed a semi-supervised learning method to train classifier, but it is unfeasible for various kinds and great number of Web data since it still needs a part of hand-labeled data. In 2012, Li et al. invented a fully automatic categorization approach for text(FACT)based on supervised learning, where no manual labeling efforts are required. But automatically labeling all data can bring noise into experiment and cause the fact that the result cannot meet the accuracy requirement. We put forward a new idea that part of data with high accuracy can be automatically tagged based on the semantic of category name, then a semi-supervised way is taken to train classifier with both labeled and unlabeled data,and ultimately a precise classification of massive text data can be achieved. The empirical experiments show that the method outperforms the supervised support vector machine(SVM) in terms of both F1 performance and classification accuracy in most cases. It proves the effectiveness of the semi-supervised algorithm in automatic TC.

关键词： text data mining semi-supervised automatic tagging classifier

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O2iJoin： An Efficient Index-Based Algorithm for Overlap Interval Join

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Journal of Computer Science & Technology 2018年第5期33卷 1023-1038页

作者： Ji-Zhou Luo Sheng-Fei Shi Guang Yang Hong-Zhi Wang Jian-Zhong Li School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China Guangdong Key Laboratory of Popular High Performance Computers Key Laboratory of Service Computing and Application Shenzhen 518000 China

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B＋-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index （i.e., O2i Index）. It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

关键词： overlap interval join temporal relation overlap inverted index join algorithm

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Improved Consistent Elastic Image Registration Based on Landmark Correspondence

Improved Consistent Elastic Image Registration Based on Land...

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Hua Long Jianbing Yi Xuan Yang School of Computer and Software Engineering Shenzhen Key Laboratory of Service Computing and ApplictionShenzhen University

ISBN: (纸本)9781467321969

Consistent image registration jointly estimates the forward and reverse transformation between two images while constraining these transforms to be inverses of one another. The consistent image registration produces mapping functions that have less pair-wise registration error and reflect the anatomical relationship of image transformation. This paper improves the consistent image algorithm based on landmark proposed by Johnson. After the convergence of consistent image registration proposed by Johnson, our method inserts new landmarks at the position where has large consistent error, in order to reduce the consistency error as a whole. Where and how to insert new landmarks are discussed in details in this paper. The Experimental results demonstrate that our algorithm is effective to reduce the consistent error and improve the registration accuracy concurrently.

关键词： Consistent image registration Landmark Thin- plate spline

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Huge Page Friendly Virtualized Memory Management

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Journal of Computer Science & Technology 2020年第2期35卷 433-452页

作者： Sai Sha Jing-Yuan Hu Ying-Wei Luo Xiao-Lin Wang Zhenlin Wang School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China Peng Cheng Laboratory Shenzhen 518052China Shenzhen Key Laboratory for Cloud Computing Technology&Applications School of Electronic and Computer EngineeringPeking University ShenzhenShenzhen 518000China Department of Computer Science Michigan Technological UniversityMichigan 49246U.S.A.

With the rapid increase of memory consumption by applications running on cloud data centers,we need more efficient memory management in a virtualized *** huge pages becomes more critical for a virtual machine's performance when it runs large working set size *** with large working set sizes are more sensitive to memory allocation,which requires us to quickly adjust the virtual machine's memory to accommodate memory phase *** would be much more efficient if we could adjust virtual machines'memory at the granularity of huge ***,existing virtual machine memory reallocation techniques,such as ballooning,do not support huge *** addition,in order to drive effective memory reallocation,we need to predict the actual memory demand of a virtual *** find that traditional memory demand estimation methods designed for regular pages cannot be simply ported to a system adopting huge *** to adjust the memory of virtual machines timely and effectively according to the periodic change of memory demand is another challenge we *** paper proposes a dynamic huge page based memory balancing system(HPMBS)for efficient memory management in a virtualized *** first rebuild the ballooning mechanism in order to dispatch memory in the granularity of huge *** then design and implement a huge page working set size estimation mechanism which can accurately estimate a virtual machine's memory demand in huge pages *** these two mechanisms,we finally use an algorithm based on dynamic programming to achieve dynamic memory *** show that our system saves memory and improves overall system performance with low overhead.

关键词： virtualization huge page ballooning memory balancing

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A light-weight hot data identification scheme via grouping-based LRU lists 15th

A light-weight hot data identification scheme via grouping-b...

引用

15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Shen, Biaobiao Li, Yongkun Xu, Yinlong Pan, Yubiao School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Guangdong Key Laboratory of Popular High Performance Computers Shenzhen518060 China Shenzhen Key Laboratory of Service Computing and Applications Shenzhen518060 China

ISBN: (纸本)9783319271392

Real-world workloads generally exhibit high skewness in access patterns, and it is a consensus that separating hot and cold data may greatly improve storage system performance such as Solid State Drive (SSD) garbage collection(GC) performance. To achieve this, the key issue is how to accurately identify hot data, which is really challenging due to the large diversity and dynamics of workloads. In this paper, we propose a light-weight and high-accuracy identification scheme, which is developed via a group of Least Recently Used (LRU) lists and requires only a small amount of memory and CPU cycles. We further deploy our scheme on SSDs with DiskSim simulator, and results show that comparing to two state-of-the-art identification schemes, our scheme further reduces SSD GC cost by up to 59.1% (62.1%), and saves 44.3% (77.5%) of computational cost. Due to the light-weight and parameter-insensitive feature, our scheme can be easily deployed at various system levels and adaptable to different workloads. © 2015 Springer International Publishing Switzerland.

关键词： Refuse collection

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Progressive image registration based on probability boosting tree

Progressive image registration based on probability boosting...

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4th International Conference on Artificial Intelligence and Computational Intelligence, AICI 2012

作者： Guo, LiTing Yi, JianBing Yang, Xuan School of Computer and Software Engineering Shenzhen Key Laboratory of Service Computing and Appliction Shenzhen University Shenzhen 518060 China

ISBN: (纸本)9783642334771

This paper proposed a novel progressive image registration method based on landmarks. At first, identification of corresponding control points is implemented by classifying of image features. We constructed a Probability Boosting Tree (PBT) to calculate the matching probability of each pair of corresponding control points and selected the highly matched ones as control points. Next, a progressive image registration is performed by inserting new landmarks selected by PBT one by one, which estimates the deformation model more and more accurate. Experimental results indicate that the proposed algorithm in this paper is feasible and can improve the precision of image registration significantly. © 2012 Springer-Verlag.

关键词： Image registration

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Image magnification based on classified training set

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Journal of Information and Computational Science 2012年第13期9卷 3647-3653页

作者： Li, Jianhong He, Huayong Liu, Hailiang State-Province Joint Laboratory of Digital Home Interactive Applications School of Information Science and Technology Sun Yat-sen University Guangzhou 510006 China Shenzhen Digital Home Key Technology Engineering Laboratory Shenzhen Key Laboratory of Digital Living Network and Content Service Research Institute of Sun Yat-sen University in Shenzhen Shenzhen 518057 China

In this paper, we propose a novel method for image magnification. On the basis of the traditional learning methods through the training set, we cluster the image patches in image set according to certain rules, for each image patch, we abstract its feature and get the enlarged image through search for the best matches patches in the defined class. This method reduces the number of image patches that needs to traverse in the learning process and improve the processing speed of the algorithm, and it excludes the image patches which are similar but very different visual to the patch to be enlarged through image patches classification and feature extraction. Experiments show that the method increases the accuracy of the search and improves the quality of the image. 1548-7741/Copyright © 2012 Binary Information Press.

关键词： Image classification

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