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Large-Scale simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Large-Scale Simulation of Quantum Computational Chemistry on...

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Supercomputing conference

作者： Honghui Shang Li Shen Yi Fan Zhiqian Xu Chu Guo Jie Liu Wenhao Zhou Huan Ma Rongfen Lin Yuling Yang Fang Li Zhuoya Wang Yunquan Zhang Zhenyu Li Chinese Academy of Sciences Institute of Computing Technology Beijing China School of Computer Science and Technology University of Science and Technology of China Hefei China Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Hefei China Shanghai Research Center for Quantum Sciences Shanghai China Hefei National Laboratory University of Science and Technology of China Hefei China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China Pilot National Laboratory for Marine Science and Technology (Qingdao) Qingdao China

ISBN: (纸本)9781665454452

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based VQE simulator to reduce the amount of memory needed and increase the simulation efficiency; (2) a combination of the Density Matrix Embedding Theory with the MPS-based VQE simulator to further extend the simulation range; (3) A three-level parallelization scheme to scale up to 20 million cores; (4) Usage of the Julia script language as the main programming language, which both makes the programming easier and enables cutting edge performance as native C or Fortran; (5) Study of real chemistry systems based on the VQE simulator, achieving nearly linearly strong and weak scaling. Our simulation demonstrates the power of VQE for large quantum chemistry systems, thus paves the way for large-scale VQE experiments on near-term quantum computers.

关键词： Technological innovation Quantum computing Computational modeling Quantum chemistry Qubit Quantum mechanics Programming

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Adaptive Cognitive Management and Knowledge Discovery Framework Based on Internet of Things Big Data 7

Adaptive Cognitive Management and Knowledge Discovery Framew...

引用

2020 7th international conference on computer-Aided Design, Manufacturing, modeling and simulation, CDMMS 2020

作者： Bai, Runxuan Lv, JianYu Shang, Chunnan Faculty of Science and Engineering University of Nottingham Ningbo Zhejiang315100 China School of Information University of International Business and Economics Southwestern University of Finance and Economics

In the future of large-scale industrial automation applications in the Internet of Things big data management and knowledge discovery, the importance of the industrial Internet is increasing day by day. The Internet of Things (IoT), computing intelligence, machine type communication, big data and sensor technology and other diversification. Technology can be combined to improve the efficiency of data management and knowledge discovery for large-scale automated applications. To this end, we need to propose a cognitive-oriented IoT big data framework (COIB-framework) and its implementation architecture, IoT big data hierarchical architecture, data organization and knowledge exploration subsystems to achieve effective data management and knowledge discovery suitable for large-scale industrial automation applications. In this work, we need to propose a cognitive-oriented IoT big data framework (COIB-framework), as well as implementation architecture, IoT big data layered architecture, data organization and knowledge mining subsystems, to meet the needs of large-scale industrial automation applications. Discussion and analysis show that the proposed framework and architecture are based on the intelligent industrial application of IoT big data provides a reasonable solution. © Published under licence by IOP Publishing Ltd.

关键词： Big data

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A fine-grained petri model for SQL time-blind injection

A fine-grained petri model for SQL time-blind injection

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2021 international conference on Networking and Network Applications, NaNA 2021

作者： Zhang, Guiling Zhang, Yaling Wang, Yichuan Zhu, Lei Ji, Wenjiang Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665441582

With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security. © 2021 IEEE.

关键词： Petri nets

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A Malaria Control Model using Mobility Data: An Early Explanation of Kedougou Case in Senegal 10

A Malaria Control Model using Mobility Data: An Early Explan...

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10th international conference on simulation and modeling Methodologies, Technologies and Applications (SIMULTECH)

作者： Khiri, Lynda Bouzid Gueye, Ibrahima Naacke, Hubert Sarr, Idrissa Gancarski, Stephane Sorbonne Univ Lab Informat Paris 6 LIP6 F-75005 Paris France Cheikh Anta Diop Univ Dept Math & Comp Sci BP 5005 Dakar Senegal Ecole Polytech Thies LTISI Thies Senegal

ISBN: (纸本)9789897584442

Studies in malaria control cover many areas such as medicine, sociology, biology, mathematics, physics, computer science and so forth. Researches in the realm of mathematics are conducted to predict the occurrence of the disease and to support the eradication process. Basically, the modeling methodology is predominantly deterministic and based on differential equations which take into account important clinical and biological features. Yet, if the individual characteristics matter when modeling the disease, the overall estimation of the epidemic evolution relies on a non-specified percentage of the global population : it is not based on the state of health of each individual. The goal of this paper is to propose a model that relies on a daily evolution of the individual's state, which depends on their mobility and the characteristics of the area they visit. Thus, the mobility data of a single person moving from one area to another, gathered thanks to mobile networks, is the essential building block to predict the disease outcome. We implement our solution and demonstrate its effectiveness through empirical experiments. The results show how promising the model is in providing possible insights into the failure of the disease control in the Kedougou region.

关键词： Malaria Control Mobility Model Discrete simulation Data Analysis

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Adaptive path planning control of snake-like robot based on reinforcement tracking learning 6

Adaptive path planning control of snake-like robot based on ...

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6th IEEE international conference on Intelligent Computing and Signal Processing, ICSP 2021

作者： Zhang, Xiaojun Institute of Computer Control Technology Huali College Guangdong University of Technology Guangzhou China

ISBN: (纸本)9780738143705

The path adaptive planning control of snake-like robot is influenced by uncertain disturbance of environment variables and joint mechanism components, which leads to poor stability of path planning parameter output. In order to improve the stability of path adaptive planning control of snake-like robot, a path adaptive planning control method of snake-like robot based on reinforcement tracking learning is proposed. The kinematics model of snake-like robot path adaptive planning is constructed, constraint parameter modeling of snake-like robot path adaptive planning control is carried out, path distribution parameters of snake-like robot are collected by using sensitive components such as attitude sensor information output instrument and accelerometer, and path spatial distribution parameters of snake-like robot are fused by using enhanced tracking learning method and input into time control actuator. Aiming at the error of unknown disturbance in path planning parameter control of snake-like robot, attitude parameter fusion and inertia error correction are carried out by using enhanced parameter fusion learning method, so as to realize stable control of snake-like robot path adaptive planning. simulation results show that this method has good stability in path adaptive planning control of snake-like robot, strong positioning and tracking ability of attitude parameters, and improves the robustness of snake-like robot moving forward. © 2021 IEEE.

关键词： Error correction

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Bridge Crack Detection Method Combining CNN and Trans-former 24

Bridge Crack Detection Method Combining CNN and Trans-former

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Proceedings of the 2024 4th international conference on Computational modeling, simulation and Data Analysis

作者： Rong Cheng Wenzhong Zhu Qianwen Yuan School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin Sichuan China

ISBN: (纸本)9798400711831

Regular inspection of bridge cracks is a crucial but labour-intensive task, which is aimed at the problems of complex background noise interference, fine crack features, and the bridge cracks are composed of multiple materials, with a large number of misdetections and omissions, which This paper proposes a novel crack segmentation model by integrating ResNet-50 and Swin Transformer V2 as the encoder. A feature fusion module is designed to efficiently detect cracks in the bridge. feature fusion module is designed to efficiently aggregate intermediate layer features from both branch encoders and connect them to the corresponding A feature fusion module is designed to efficiently aggregate intermediate layer features from both branch encoders and connect them to the corresponding layer decoder, enhancing the detailed feature information. Sub-pixel convolution is introduced in the decoder for up-sampling to better restore the details of the cracks. Sub-pixel convolution is introduced in the decoder for up-sampling to better restore the details of the cracks. Finally, the model is trained using a combined loss function to alleviate the crack sample imbalance problem. results demonstrate that on the public Bridge Crack Library (BCL) dataset, the proposed model achieves precision, F1-score, and mean Intersection over Union (mIoU) of the cracks. Union (mIoU) of 77.84%, 79.17%, and 82.07% respectively. Compared to ten other models, it achieves the best performance, effectively mitigating false detections and missed detections, and significantly improving the detection accuracy of fine cracks.

关键词： convolutional neural network

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A NON-INTRUSIVE REDUCED ORDER MODEL FOR NEUTRONIC TRANSIENT ANALYSES OF THE ALFRED REACTOR

A NON-INTRUSIVE REDUCED ORDER MODEL FOR NEUTRONIC TRANSIENT ...

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2021 international conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2021

作者： Abrate, Nicolò Dulla, Sandra Pedroni, Nicola Ravetto, Piero Politecnico di Torino Dipartimento Energia NEMO Group Corso Duca degli Abruzzi 24 Torino10129 Italy

ISBN: (纸本)9781713886310

The safety analysis of Gen-IV lead-cooled fast reactors requires accurate evaluations for different operational and accidental conditions. The FRENETIC code, constituted by a full-core, multi-group nodal diffusion module coupled with a thermal-hydraulics one, allows to perform accurate transient calculations, but it is not suitable for the detailed parametric evaluations required by a thorough safety assessment. To fill this gap in the code performances, a non-intrusive reduced-order model reproducing an accurate approximation of the FRENETIC output with a reduced computational effort is proposed. The results obtained for a stand-alone neutronic transient involving the accidental insertion of a control rod show how the approach adopted is promising for computationally-efficient safety assessments. Copyright © 2021 AMERICAN NUCLEAR SOCIETY, INCORPORATED, LA GRANGE PARK, ILLINOIS *** rights reserved.

关键词： accidental transient analysis FRENETIC full-core simulation improved quasi-static lead cooled fast reactors non-intrusive reduced-order modeling POD RBF

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Towards Optimized Hydrological Forecast Prediction of WRF-Hydro on GPU

Towards Optimized Hydrological Forecast Prediction of WRF-Hy...

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IEEE international conference on High Performance Computing and Communications (HPCC)

作者： Hongrui Liu Kelun Lei Hailong Yang Zhongzhi Luan Depei Qian School of Computer Science and Engineering Beihang University Beijing China

Hydrological models are designed to simulate and predict the dynamic cycling of surface and groundwater. These models represent simplifications of complex hydrological pro-cesses and play an integral role in hydrological research and real-world applications. The WRF Hydro model is widely used due to its multi-scale capabilities for land-atmosphere modeling. Recent studies have focused on enhancing the performance of different modules in WRF through GPU parallel optimization techniques. However, WRF - Hydro has not been able to utilize the GPU for computations which leads to lower computational efficiency when simulating hydrological calculations over large regions for extended periods. In this paper, we first analyze the computational and communication bottlenecks in WRF Hydro. We then propose several methods for performance optimization accordingly. To address the computational bottleneck, we develop efficient GPU-based overland routing and channel routing, eliminating redundant calculations. Additionally, we adopt an improved block partitioning strategy to enhance memory access efficiency on the GPU. To tackle the communicational bottleneck, we adopt a packetized communication strategy to reduce the number of communication operation calls in channel routing and simplify the preprocessing operations for communication. Meanwhile, for overland routing, we employ multi-stream to minimize redundant synchronization operations and overlap communication with computation to reduce communication overhead. The evaluation results demonstrate that WRF -Hydro can achieve significant speedups in several simulation cases after applying our proposed optimizations, resulting in a maximum speedup of 6.67 ×.

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modeling and Analysis of Framework for the Implementation of a Virtual Workplace in Nigerian Universities Using Coloured Petri Nets

Modeling and Analysis of Framework for the Implementation of...

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3rd international conference on Intelligent Computing and Optimization, ICO 2020

作者： Okpor, James Apeh, Simon T. Department of Computer Science Federal University Wukari Wukari Taraba State Nigeria Department of Computer Engineering University of Benin Benin City Edo State Nigeria

ISBN: (纸本)9783030681531

This paper presents a Hierarchical Coloured Petri Nets model specifically designed to support the implementation process of a virtual workplace in Nigerian Universities. CPN Tools 4.0 was used to capture the various phases and activities of the framework for the implementation of a virtual workplace into a Hierarchical Coloured Petri Nets (CPN) model. The developed virtual workplace CPN model has been analyzed using simulation, and state space analysis methods. The developed CPN model being a graphical and also an executable representation of the implementation framework;will assist those empowered with the responsibility of implementing a virtual workplace to have a better understanding of the implementation process. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Petri nets

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A Study on Detection of Emotions with The Help of Convolutional Neural Network

A Study on Detection of Emotions with The Help of Convolutio...

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Computational Modelling, simulation and Optimization (ICCMSO), international conference on

作者： Deepak Raj Md. Abdul Wassay Karunendra Verma Department of Computer Science Engineering Chandigarh University Mohali India Chitkara University Himachal Pradesh India

Facial Expression is a mean which can be employed to find out what is running in the mind of the person whom we talk. It is always an easy task for humans to derive some insights from the expressions. But it is a complicated task for machine to derive it by using computer Algorithms. But recent developments in the field of computer Vision and Machine Learning has enhanced the availability of resources so that it become possible to derive certain conclusions from the input we are giving to particular Machine. This paper is planning to propose an approach to find out what emotional stage the particular person is running which is termed as Facial Emotion Recognition using Convolutional Neural Network (FERC). The FERC constitutes two parts: The first part of this Convolutional Neural Network is used to remove background from an image and second part is used to extract features from facial expressions. It consist of a database of around 10,000 image. The final layer which is a perceptron which works in series with two layer Convolutional Neural Network. The Perceptron is used to adjust weights and exponent values after going through each iteration. Again a background removal is applied just before generation of Emotional Vector. This will help us to avoid from multiple problems which can occur. Using Two stage CNN model we found out accuracy near about 84.92 percentage which is a better value based on insights from 24 values. The Two Layer CNN works in series where remaining layer is used to adjust weights after each iteration. FERC follows an exactlynewapproach compared to Single stage CNN and thus enhancing the accuracy. Further it uses a novel background elimination procedure compared to technology of EV earlier existed which could avoid coping with coping with more than one troubles that may occur. FERC is employed with about more than 28K pictures by using FER2013 dataset. We can make use of FERC in many cases including predictive learning, Lie detection… etc.

关键词： Emotion recognition Computational modeling Feature extraction Stability analysis Software Real-time systems Planning

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