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International Conference on data engineering

作者： Zirui Liu Chaozhe Kong Kaicheng Yang Tong Yang Ruijie Miao Qizhi Chen Yikai Zhao Yaofeng Tu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China ZTE Corporation

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced.

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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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Research of Improved TD3 Robotic Arm Path Planning using Evolutionary Algorithm 5

Research of Improved TD3 Robotic Arm Path Planning using Evo...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Zhang, Feng Xiong, Jianqiang Yuan, Shuai Wen, Ke Shenyang Jianzhu University Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Electrical and Control Engineering Shenyang110168 China Shenyang Jianzhu University School of Computer Science and Engineering Shenyang110168 China

ISBN: (纸本)9798350307320

Nowadays, application of automated intelligent robot arm devices to improve industrial production efficiency has become a popular research field in the world. The previous off-line path planning method of robotic arm has the inadequacies of low efficiency and slow speed. Although the deep reinforcement learning has accomplished many achievements in the path planning of control manipulator, there are still some problems such as long training time and low planning accuracy. To solve the abovementioned issues, we propose an improved Twin Delayed Deep Deterministic policy gradient (TD3) algorithm (Improved Cross-Entropy Method-TD3: ICEM-TD3) for the path planning of the robotic arm. First, this paper combines evolutionary strategies with TD3 to generate action networks. Then the exploration of TD3 algorithm in the action space is replaced by the exploration in the parameter space. In addition, this paper designs a new reward function to weaken the redundancy of planning and accelerate the convergence speed of training. Finally, the Gazebo simulator is adopted to verify the proposed algorithm, and the results illustrate that the proposed algorithm can greatly improve the accuracy of the path planning of the manipulator using deep reinforcement learning. © 2023 IEEE.

关键词： Motion planning

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big data Technology in Intelligent Distribution Network: Demand and applications 6th

Big Data Technology in Intelligent Distribution Network: De...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Ye, Zhi-Peng Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou China Fuzhou University No. 33 Xuefu South Road New District FuzhouFujian350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

Nowadays, with the development and maturity of big data technology, big data analysis technology is more and more widely used in practice, and more and more data are gradually applied to the smart grid of our country. Since the data in the smart grid meets the 4V characteristics of big data (large quantity, fast speed, many types, low value density), the use of big data technology can provide more accurate and cheaper data for power information generation Economic value and significance. Through the analysis of the development process of Chinas power industry, the development of distribution network in China obviously lags behind the development of power generation and transmission network. At present, more than 95% of the blackouts are caused by the distribution network, and half of the power loss occurs in the distribution network, so the automation of the distribution network system urgently needs the support of new technologies. This paper first enumerates several key points of big data technology, including big data collection, storage and analysis, and then expounds several methods of big data analysis. On this basis, big data technology is applied to the field of intelligent distribution network. Especially in the application of distribution forecasting, it can provide more powerful technical support for the operation of smart distribution network, continuously improve the technical level of Chinas smart distribution network, and promote the optimization and upgrading of smart grid system. Finally, an optimized prediction model is proposed, and the application of the new technology (5G technology) developed at the present stage is prospected, and its contribution to the data acquisition and application of big data technology is analyzed. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： big data

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Time-Series analysis on the Impact Level upon Taiwan Stock Market with Events of Signing International Agreements 3

Time-Series Analysis on the Impact Level upon Taiwan Stock M...

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3rd International Conference on Computing Measurement Control and Sensor Network, CMCSN 2016

作者： Tsai, Pei-Wei Lin, You-Xin Zhang, Jing Chang, Jui-Fang Chen, Jhih-Sheng Zhang, Jilin College of Information Science and Engineering Fujian University of Technology Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China Department of International Business National Kaohsiung University of Applied Sciences Kaohsiung Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou China

ISBN: (纸本)9781509010936

Stock investment is one of the available conventional financial commodities in the investment market. The individual price of stocks including stocks in the field of industrial, medical, financial, overall, etc. Is subject to many different aspects. The relationship between aspects, the international economy, and the stock price is complex and is not easy to be categorized. Moreover, the trend of the world increases the cooperation and links between countries and different nations. Many agreements in tourism, trade, etc. Are signed between countries. In this paper, we focus on the effect on the stock prices caused by the agreements signed between Taiwan and other countries. The time series method is utilized to analyze the effect on the stock price forecasting caused by 5 of the incidents including WTO, Taiwan-Nicaragua FTA, Taiwan-Honduras FTA, and Taiwan-Japan BIA. Both the pre-event and the post-event periods are included for observing the fluctuation behavior of the stock market price. The MAPE value is used as the criterion to verify the correctness of the forecasting result. The MAPE values obtained in all experiments are all less than 10%. It implies that the time series method presents good forecasting ability in the analyzed cases. © 2016 IEEE.

关键词： Forecasting

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Aonn-2: An Adjoint-Oriented Neural Network Method for Pde-Constrained Shape Optimization

SSRN

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SSRN 2023年

作者： Wang, Xili Yin, Pengfei Zhang, Bo Yang, Chao School of Mathematical Sciences Peking University Beijing100871 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China PKU-Changsha Institute for Computing and Digital Economy Hunan410006 China

Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore encounter challenges due to mesh deformation. To overcome this limitation, we present a new adjoint-oriented neural network method, AONN-2, for PDE-constrained shape optimization problems. This method extends the capabilities of the original AONN method [1], which is developed for efficiently solving parametric optimal control problems. AONN-2 inherits the direct-adjoint looping (DAL) framework for computing the extremum of an objective functional and the neural network methods for solving complicated PDEs from AONN. Furthermore, AONN-2 expands the application scope to shape optimization by taking advantage of the shape derivatives to optimize the shape represented by discrete boundary points. AONN-2 is a fully mesh-free shape optimization approach, naturally sidestepping issues related to mesh deformation, with no needs for maintaining mesh quality and additional mesh corrections. A series of experimental results are presented, highlighting the flexibility, robustness, and accuracy of AONN-2. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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AONN-2: An adjoint-oriented neural network method for PDE-constrained shape optimization

arXiv

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arXiv 2023年

Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore encounter challenges due to mesh deformation. To overcome this limitation, we present a new adjoint-oriented neural network method, AONN-2, for PDE-constrained shape optimization problems. This method extends the capabilities of the original AONN method [1], which is developed for efficiently solving parametric optimal control problems. AONN-2 inherits the direct-adjoint looping (DAL) framework for computing the extremum of an objective functional and the neural network methods for solving complicated PDEs from AONN. Furthermore, AONN-2 expands the application scope to shape optimization by taking advantage of the shape derivatives to optimize the shape represented by discrete boundary points. AONN-2 is a fully mesh-free shape optimization approach, naturally sidestepping issues related to mesh deformation, with no need for maintaining mesh quality and additional mesh corrections. A series of experimental results are presented, highlighting the flexibility, robustness, and accuracy of AONN-2. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A regularized approach to sparse optimal policy in reinforcement learning

arXiv

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arXiv 2019年

作者： Li, Xiang Yang, Wenhao Zhang, Zhihua School of Mathematical Sciences Peking University Beijing China Center for Data Science Peking University Beijing China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University Beijing China

We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term. The extant entropy-regularized MDPs can be cast into our framework. Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning. In particular, we present sufficient and necessary conditions that induce a sparse optimal policy. We also conduct a full mathematical analysis of the proposed regularized MDPs, including the optimality condition, performance error, and sparseness control. We provide a generic method to devise regularization forms and propose off-policy actor critic algorithms in complex environment settings. We empirically analyze the numerical properties of optimal policies and compare the performance of different sparse regularization forms in discrete and continuous environments. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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An auto code generator for stencil on SW26010 21

An auto code generator for stencil on SW26010

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation

arXiv

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arXiv 2024年

作者： Zhang, Bo Yang, Chao National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China School of Mathematical Sciences Peking University Beijing100871 China PKU Changsha Institute for Computing and Digital Economy Changsha410205 China

In recent years, the researches about solving partial differential equations (PDEs) based on artificial neural network have attracted considerable attention. In these researches, the neural network models are usually designed depend on human experience or trial and error. Despite the emergence of several model searching methods, these methods primarily concentrate on optimizing the hyperparameters of fully connected neural network model based on the framework of physics-informed neural networks (PINNs), and the corresponding search spaces are relatively restricted, thereby limiting the exploration of superior models. This article proposes an evolutionary computation method aimed at discovering the PINNs model with higher approximation accuracy and faster convergence rate. In addition to searching the numbers of layers and neurons per hidden layer, this method concurrently explores the optimal shortcut connections between the layers and the novel parametric activation functions expressed by the binary trees. In evolution, the strategy about dynamic population size and training epochs (DPSTE) is adopted, which significantly increases the number of models to be explored and facilitates the discovery of models with fast convergence rate. In experiments, the performance of different models that are searched through Bayesian optimization, random search and evolution is compared in solving Klein-Gordon, Burgers, and Lamé equations. The experimental results affirm that the models discovered by the proposed evolutionary computation method generally exhibit superior approximation accuracy and convergence rate, and these models also show commendable generalization performance with respect to the source term, initial and boundary conditions, equation coefficient and computational domain. The corresponding code is available at https://***/MathBon/Discover-PINNs-Model. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural network models

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