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Atmosphere air temperature forecasting using the honey badger optimization algorithm: on the warmest and coldest areas of the world

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engineering Applications of Computational Fluid Mechanics 2023年第1期17卷

作者： Zhou, Jincheng Wang, Dan Band, Shahab S. Mirzania, Ehsan Roshni, Thendiyath School of Computer and Information Qiannan Normal University for Nationalities Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province Duyun China Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun China School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun China Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan Department of Water Engineering University of Tabriz Tabriz Iran Department of Civil Engineering National Institute of Technology Patna Bihar India

Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R², and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Air temperature artificial neural network forecasting honey badger algorithm hybrid model warmest and coldest regions

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Formation control of multiple nonholonomic vehicles with local measurements in 3D space

Formation control of multiple nonholonomic vehicles with loc...

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IEEE Conference on Decision and Control

作者： Shuai Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China National Engineering Research Center of Software Engineering Peking University Beijing China

ISBN: (纸本)9781665436601

We study the formation control problem for multiple nonholonomic vehicles in 3D space, where vehicles are required to maintain a prescribed pattern and rotate around a target in the target plane, with the direction of rotation along with the normal vector of the target plane. Unlike the most existing works that study formation problems of agents modeled by mass-point in a 2D plane, control of multiple nonholonomic vehicles in 3D space means a grand challenge since both the space and dynamics are complex. We give explicit formulations of the problem and vehicles’ dynamics. Then, control laws to solve the problem are proposed and further transformed into the body-fixed frames of vehicles to show that they can be easily implemented using only local measurements and the direction of gravity. Theoretical analysis is presented and simulations show the effectiveness of the proposed laws.

关键词： Space vehicles Solid modeling Analytical models Three-dimensional displays Conferences Aerospace electronics Extraterrestrial measurements

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Gathering github oss requirements from q&a community: An empirical study

arXiv

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arXiv 2020年

作者： Huang, Hao Lu, Yao Mao, Xinjun Key Laboratory of Software Engineering for Complex Systems College of Computer National University of Defense Technology Changsha410073 China

ross-community collaboration can exploit the expertise and knowledges of crowds in different communities. Recently increasing users in open source software (OSS) community like GitHub attempt to gather software requirements from question and answer (Q&A) communities such as Stack Overflow (SO). In order to investigate this emerging cross-community collaboration phenomenon, the paper presents an exploratory study on cross-community requirements gathering of OSS projects in GitHub. We manually sample 3266 practice cases and quantitatively analyze the popularity of the phenomenon, the characteristics of the gathered requirements, and collaboration behaviors of cross-community. Some important findings are obtained: more than half of the requirements gathered from SO are enhancements and the majority of the gathered requirements are non-functional requirements. In addition, OSS developers can directly obtain related solutions and contributions of the gathered requirements from SO in the gathering process. Copyright © 2020, The Authors. All rights reserved.

关键词： Requirements engineering

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Graph-based Vulnerability Detection via Extracting Features from Sliced Code

Graph-based Vulnerability Detection via Extracting Features ...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Peng Wu Liangze Yin Xiang Du Liyuan Jia Wei Dong Key Laboratory Of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

With the development of open source software and open source community, there are more available codes on the Internet. And the open vulnerability information can be found on the Internet. In fact, using known vulnerabilities to calculate the similarity with the source code has been demonstrated a useful method to detect vulnerabilities. But the vulnerabilities often have many irrelevant codes, which may cause false positives and reduce the accuracy of vulnerability detection. Besides, the program code may have been patched. This also leads to false positives. We use code property graphs to extract source code and calculate the similarity between the vulnerable code and the source code to judge whether the software has vulnerabilities. By using the patched code, we can reduce the false positive. We use our approach on LibTIFF and Linux kernel. The experimental results show that the approach can effectively find vulnerabilities and reduce the false positive.

关键词： Linux software quality Feature extraction Internet software reliability Security Open source software

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Vulnerability Analysis through Interface-based Checker Design

Vulnerability Analysis through Interface-based Checker Desig...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Xiang Du Liangze Yin Peng Wu Liyuan Jia Wei Dong Key Laboratory Of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

Checkers design is a main step for static analysis of different vulnerabilities. This paper focuses on static analysis on code property graph, which combines abstract syntax tree, control flow graph, and program dependence graph. Developing checkers on code property graph directly is usually complex and difficult. In this paper, we have analyzed a large number of checkers of different vulnerabilities, and extracted those most commonly used operations as a set of interfaces. We have implemented these interfaces and developed a set of vulnerability checkers based on them. The practical efficacy of these checkers are evaluated on the Linux kernel source code. Experimental results show that our proposed interfaces are strong enough to express most vulnerabilities and our implementation is effective for vulnerabilities detection.

关键词： Linux software algorithms Static analysis software quality Syntactics software reliability Kernel

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An integrated framework for accelerating reactive flow simulation using GPU and machine learning models

arXiv

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arXiv 2023年

作者： Mao, Runze Wang, Yingrui Zhang, Min Li, Han Xu, Jiayang Dong, Xinyu Zhang, Yan Chen, Zhi X. State Key Laboratory of Turbulence and Complex Systems Aeronautics and Astronautics College of Engineering Peking University Beijing100871 China Beijing100080 China Shanghai SenseTime Intelligent Technology Co. LTD Shanghai200233 China CAEP Software Center for High Performance Numerical Simulation Beijing100088 China Institute of Applied Physics and Computational Mathematics Beijing100088 China

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU. © 2023, CC BY.

关键词： Graphics processing unit

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Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

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engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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Stochastic Event-triggered Variational Bayesian Filtering

arXiv

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arXiv 2022年

作者： Lv, Xiaoxu Duan, Peihu Duan, Zhisheng Chen, Guanrong Shi, Ling The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University Beijing100871 China Department of Electrical Engineering City University of Hong Kong Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process noise covariances and an initial measurement noise covariance, a variational Bayesian method and a fixed-point iteration method are utilized to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The proposed algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. Finally, the performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle. Copyright © 2022, The Authors. All rights reserved.

关键词： Kalman filters

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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作者： Huawei ZHU Yihao MAO Yiming ZHAO Liyuan SHI Yanli SHAO School of Information and Electrical Engineering Zhejiang University City College Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and TechnologyHangzhou Dianzi University

With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has become the main means to alleviate traffic ***-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation,and has become one of the research hotspots in intelligent transportation ***,A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction ***,a graph convolutional network(GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow ***,the long and short-term memory(LSTM) neural network is used to process the time ***,the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow *** results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method,and can provide a basis for intelligent traffic control and smart city construction.

关键词： Traffic Flow Prediction Spatio-temporal GCN LSTM

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