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arXiv 2021年

作者： Xia, Shuyin Bai, Xinyu Wang, Guoyin Meng, Deyu Gao, Xinbo Chen, Zizhong Giem, Elisabeth The Chongqing Key Laboratory of Computational Intelligence Chongqing University of Telecommunications and Posts Chongqing 400065 China The National Engineering Laboratory for Algorithm and Analysis Technologiy on Big Data Xi'an Jiaotong University Xi'An710049 China The Department of Computer Science and Engineering University of California Riverside RiversideCA92521 United States

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative *** proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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A New Optimization Based on Parallelizing Hybrid PSOGSA Algorithm 16th

A New Optimization Based on Parallelizing Hybrid PSOGSA Algo...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, applications and Tools, FITAT 2020

作者： Yu, Jie Wang, Hong-Jiang Pan, Jeng-Shyang Chang, Kuo-Chi Ngo, Truong-Giang Nguyen, Trong-The College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Computer Science and Engineering Shandong University of Science and Technology Shandong China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Business Administration North Borneo University College Sabah Malaysia Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9789813367562

This study suggests a new metaheuristic algorithm for global optimization, based on parallel hybridizing the swarm optimization (PSO) and Gravitational search algorithm (GSA). Subgroups of the population are formed by dividing the swarm’s community. Communication between the subsets can be developed by adding strategies for the mutation. Twenty-three benchmark functions are used to test its performance to verify the feasibility of the proposed algorithm. Compared with the PSO, GSA, and parallel PSO (PPSO), the findings of the proposed algorithm reveal that the proposed PPSOGSA achieves higher precision than other competitor algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Global optimization

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C olumnSGD: A Column-oriented Framework for Distributed Stochastic Gradient Descent

C olumnSGD: A Column-oriented Framework for Distributed Stoc...

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International Conference on data engineering

作者： Zhipeng Zhang Wentao Wu Jiawei Jiang Lele Yu Bin Cui Ce Zhang School of EECS & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Microsoft Research Redmond USA ETH Zurich Switzerland Tencent Inc Center for Data Science Peking University & National Engineering Laboratory for Big Data Analysis and Applications

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Distributed machine learning (ML) has triggered tremendous research interest in recent years. Stochastic gradient descent (SGD) is one of the most popular algorithms for training ML models, and has been implemented in almost all distributed ML systems, such as Spark MLlib, Petuum, MXNet, and TensorFlow. However, current implementations often incur huge communication and memory overheads when it comes to large models. One important reason for this inefficiency is the row-oriented scheme (RowSGD) that existing systems use to partition the training data, which forces them to adopt a centralized model management strategy that leads to vast amount of data exchange over the network. We propose a novel, column-oriented scheme (ColumnSGD) that partitions training data by columns rather than by rows. As a result, ML model can be partitioned by columns as well, leading to a distributed configuration where individual data and model partitions can be collocated on the same machine. Following this locality property, we develop a simple yet powerful computation framework that significantly reduces communication overheads and memory footprints compared to RowSGD, for large-scale ML models such as generalized linear models (GLMs) and factorization machines (FMs). We implement ColumnSGD on top of Apache Spark, and study its performance both analytically and experimentally. Experimental results on both public and real-world datasets show that ColumnSGD is up to 930× faster than MLlib, 63× faster than Petuum, and 14× faster than MXNet.

关键词： Conferences data engineering Radio frequency

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An Improved Whale Optimization Algorithm and Its Application to Power Generation in Cascade Reservoir 16th

An Improved Whale Optimization Algorithm and Its Application...

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作者： Lü, Ji-Xiang Yan, Li-Jun Pan, Tien-Szu Chu, Shu-Chuan Pan, Jeng-Shyang He, Xian-Kang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Shenzhen Institute of Information Technology Shenzhen518172 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung82445 Taiwan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Science and Engineering Flinders University Sturt Rd Bedford ParkSA5042 Australia Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350108 China College of Mechanical & Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Business Administration North Borneo University College Sabah Malaysia

ISBN: (纸本)9789813364196

Nowadays, there is a very popular artificial intelligence algorithm called whale optimization algorithm (WOA). WOA is obtained through the special bubble net foraging process of humpback whales. It has a special search mechanism, and is very helpful to solve some complex optimization problems and large scale. An improved WOA is implemented to optimize the cascade reservoir power generation. Firstly, by introducing nonlinear time-varying adaptive weights, the WOA performance in the local optimization and global exploration stages is improved;secondly, the differential mutation perturbation factor is introduced in the shrinking and surrounding stage of the whale algorithm to prevent premature convergence. In addition, the logarithmic spiral search method of whale individuals has been improved so that the ability to solve the algorithm traversal can be found. Experimental results show that it has a great improvement in accuracy and convergence compared with the original WOA in the optimal dispatch model of cascade reservoir power generation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optimization

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The Incoming Influenza Season—China,the United Kingdom,and the United States,2021–2022

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China CDC weekly 2021年第49期3卷 1039-1045页

作者： Shasha Han Ting Zhang Yan Lyu Shengjie Lai Peixi Dai Jiandong Zheng Weizhong Yang Xiaohua Zhou Luzhao Feng Beijing International Center for Mathematical Research Peking UniversityBeijingChina Harvard Medical School Harvard UniversityBostonMAUSA School of Population Medicine and Public Health Chinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingChina WorldPop School of Geography and Environmental ScienceUniversity of SouthamptonSouthamptonUK Division for Infectious Diseases Chinese Center for Disease Control and PreventionBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina.

Introduction:Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)*** has been scarce information to understand the future dynamics of influenza—and under different hypothesis on relaxation of nonpharmaceutical interventions(NPIs)in particular—after the disruptions to seasonal ***:We collected data from public sources in China,the United Kingdom,and the United States,and forecasted the influenza dynamics in the incoming 2021–2022 season under different *** considered Northern China and Southern China separately,due to the sharp difference in the patterns of seasonal *** the United Kingdom,data were collected for England ***:Compared to the epidemics in 2017–2019,longer and blunter influenza outbreaks could occur should NPIs be fully lifted,with percent positivity varying from 10.5 to 18.6 in the studying *** rebounds would be smaller if the maskwearing intervention continued or the international mobility stayed low,but sharper if the mask-wearing intervention was lifted in the middle of influenza ***,influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza ***:The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019(COVID-19)*** light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society,our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control,especially for seasons after long periods of low-exposure to influenza *** influenza activity declines globally during the coronavirus disease 2019(COVID-19)pandemic(1–4).For instance,in China,influenza activity,as measured by percentage of submitted specimens testing

关键词： prevention globally continued

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Global static stability and its relation to gravity waves in the middle atmosphere

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Earth and Planetary Physics 2020年第5期4卷 504-512页

作者： Xiao Liu JiYao Xu Jia Yue Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control School of Mathematics and Information SciencesHenan Normal UniversityXinxiang Henan 453007China State Key Laboratory of Space Weather Center for Space Science and Applied ResearchChinese Academy of SciencesBeijing 100190China School of Astronomyand Space Science University of the Chinese Academy of ScienceBeijing 100049China Catholic University of America WashingtonDC 20064USA National Aeronautics and Space Administration(NASA)Goddard Space Flight Center GreenbeltMD 20771USA

The global atmospheric static stability(N2)in the middle atmosphere and its relation to gravity waves(GWs)were investigated by using the temperature profiles measured by the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instrument from 2002 to *** low latitudes,a layer with enhanced N2 occurs at an altitude of^20 km and exhibits annual oscillations caused by tropopause inversion *** an altitude of^70 km,enhanced N2 exhibits semiannual oscillations at low latitudes caused by the mesosphere inversion layers and annual oscillations at high latitudes resulting from the downward shift of the summer *** correlation coefficients between N2 and GW amplitudes can be larger than 0.8 at latitudes poleward of^40°N/*** observation provides factual evidence that a large N2 supports large-amplitude GWs and indicates that N2 plays a dominant role in maintaining GWs at least at high latitudes of the middle *** evidence also partially explains the previous results regarding the phase changes of annual oscillations of GWs at high latitudes.

关键词： atmospheric static stability gravity waves annual oscillation semiannual oscillation mesopause

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Short-term household load forecasting model based on variational mode decomposition and gated recurrent unit with attention mechanism

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 143-153页

作者： Lin, Fu Liu, Shi-Jian Chao, Han-Chieh Pan, Jeng-Shyang College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Department of Electrical Engineering National Dong Hwa University Hualien Taiwan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The household load is an important part of the load. Accurate short-term household load forecasting is of great help to issues such as power price formulation, demand response, or power transmission. This paper proposed a forecasting model based on Variational Mode Decomposition (VMD) and Gated Recurrent Unit (GRU) combined with self-attention mechanism (SAM) and attention mechanism (AM) for short-term household load forecasting. First, we use VMD to decompose household electricity load data, extract sub-sequences with strong periodic information, and reorganize the original data sets with sub-sequences and data pre-processed. The new data sets contain more abundant information. Secondly, GRU combined with SAM and AM forecasting model for learning and training to obtain results. Finally, RMSE and MAPE evaluation functions are used for method evaluation to verify the superiority of the proposed scheme. The RMSE and MAPE of the proposed method are 0.193 kW and 15.567%, respectively, which are more favorable than traditional GRU. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Variational mode decomposition

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Low-Temperature Growing Anatase TiO2/SnO2 Multi-dimensional Heterojunctions at MXene Conductive Network for High-Efficient Perovskite Solar Cells

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Nano-Micro Letters 2020年第3期12卷 199-217页

作者： Linsheng Huang Xiaowen Zhou Rui Xue Pengfei Xu Siliang Wang Chao Xu Wei Zeng Yi Xiong Hongqian Sang Dong Liang National Engineering Research Center for Agro‑Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601People’s Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco‑Dyeing and FinishingWuhan Textile UniversityWuhan 430073People’s Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro‑and Nano‑Structures and Center for Electron MicroscopyWuhan UniversityWuhan 430072People’s Republic of China Department of Physics King’s College LondonThe StrandLondon WC2R 2LSUK

A multi-dimensional conductive heterojunction structure,composited by TiO2,SnO2,and Ti3C2TX MXene,is facilely designed and applied as electron transport layer in efficient and stable planar perovskite solar *** on an oxygen vacancy scramble effect,the zero-dimensional anatase TiO2 quantum dots,surrounding on two-dimensional conductive Ti3C2TX sheets,are in situ rooted on three-dimensional SnO2 nanoparticles,constructing nanoscale TiO2/SnO2 *** fabrication is implemented in a controlled lowtemperature anneal method in air and then in N2 *** the optimal MXene content,the optical property,the crystallinity of perovskite layer,and internal interfaces are all facilitated,contributing more amount of carrier with effective and rapid transferring in *** champion power conversion efficiency of resultant perovskite solar cells achieves 19.14%,yet that of counterpart is just 16.83%.In addition,it can also maintain almost 85%of its initial performance for more than 45 days in 30–40%humidity air;comparatively,the counterpart declines to just below 75%of its initial performance.

关键词： In situ fabrication Multi-dimensional heterojunction Oxygen vacancy scramble effect Electron transport layer Perovskite solar cells

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A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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