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Preventing Pressure Oscillations Does Not Fix Local Linear Stability Issues of Entropy-Based Split-Form High-Order Schemes

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Communications on Applied Mathematics and Computation 2022年第3期4卷 880-903页

作者： Hendrik Ranocha Gregor J.Gassner Applied Mathematics Münster University of Münster48149 MünsterGermany Computer Electrical and Mathematical Science and Engineering(CEMSE)Division King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Department of Mathematics and Computer Science Center for Data and Simulation ScienceUniversity of CologneCologneGermany

Recently,it was discovered that the entropy-conserving/dissipative high-order split-form discontinuous Galerkin discretizations have robustness issues when trying to solve the sim-ple density wave propagation example for the compressible Euler *** issue is related to missing local linear stability,i.e.,the stability of the discretization towards per-turbations added to a stable base *** is strongly related to an anti-diffusion mech-anism,that is inherent in entropy-conserving two-point fluxes,which are a key ingredi-ent for the high-order discontinuous Galerkin *** this paper,we investigate if pressure equilibrium preservation is a remedy to these recently found local linear stability issues of entropy-conservative/dissipative high-order split-form discontinuous Galerkin methods for the compressible Euler *** equilibrium preservation describes the property of a discretization to keep pressure and velocity constant for pure density wave *** present the full theoretical derivation,analysis,and show corresponding numerical results to underline our *** addition,we characterize numerical fluxes for the Euler equations that are entropy-conservative,kinetic-energy-preserving,pressure-equilibrium-preserving,and have a density flux that does not depend on the *** source code to reproduce all numerical experiments presented in this article is available online(https://***/10.5281/zenodo.4054366).

关键词： Entropy conservation Kinetic energy preservation Pressure equilibrium preservation Compressible Euler equations Local linear stability Summation-by-parts

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Game theory, learning, and control systems

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National science Review 2020年第7期7卷 1118-1119页

作者： Jeff S.Shamma Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology

Game theory is the study of interacting decision makers, whereas control systems involve the design of intelligent decision-making *** many control systems are interconnected, the result can be viewed through the lens of game theory. This article discusses both long standing connections between these fields as well as new connections stemming from emerging applications.

关键词： Game theory

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Voltage Gated Domain Wall Magnetic Tunnel Junction for Neuromorphic Computing Applications

Voltage Gated Domain Wall Magnetic Tunnel Junction for Neuro...

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IEEE Conference on Nanotechnology

作者： Aijaz H. Lone Hanrui Li Nazek El-Atab Gianluca Setti Hossein Fariborzi Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology

We propose a novel spin-orbit torque (SOT) driven and voltage-gated domain wall motion (DWM)-based MTJ device and its application in neuromorphic computing. We show that by utilizing the voltage-controlled gating effect on the DWM, the access transistor can be eliminated. The device provides more control over individual synapse writing and shows highly linear synaptic behavior. The voltage-controlled DW- gating performance of the device in the crossbar array is quantified. The device long-term potentiation/depression (LTP/LTD) linearity dependence on material parameters such as DMI, at different temperatures, is evaluated for real-environment performance analysis. Furthermore, adopting the ideal and skyrmion leaky integrate and fire neuron models, we implement the spiking convolutional neural network for pattern recognition applications The DW-MTJ conductance was mapped to the weights of the SNN. When trained and tested on the CIFAR-10 data set, the architecture based on DW-MTJ synapse achieved accuracy of around 85%.

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The Effectiveness of CNN-Based Models in Macroscopic Motion Direction Detection

The Effectiveness of CNN-Based Models in Macroscopic Motion ...

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Robotics and computer Vision (ICRCV), International Conference on

作者： Zhiyu Qiu Tianqi Chen Yuxiao Hua Yuki Todo Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Kanazawa Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

Convolutional Neural Networks (CNNs) have revolutionized the field of computer vision, offering significant advancements in image and video recognition, classification, and segmentation tasks. Leveraging hierarchical pattern recognition capabilities, CNNs have demonstrated superior performance over traditional machine learning methods. One particularly critical application of CNNs is in motion direction detection, which is essential for enhancing security in surveillance systems, ensuring safe navigation in autonomous vehicles, and providing valuable insights in sports analytics. In this study, our primary goal was to investigate how different types of input data impact the accuracy of motion direction detection. We applied five distinct preprocessing methods to test the effect of input data on accuracy. Furthermore, we conducted experiments in various object and background scenarios to evaluate adaptability of simple 4-layer CNN, EfficientNetBO and ResNet50 in detecting motion direction. Finally, we tested the noise resistance of these three models. The results indicate that CNNs are capable of extracting the macroscopic motion direction of objects in images. Our study highlights the potential of CNN-based models for efficient and effective motion direction detection.

关键词： Resistance Training computer vision Adaptation models Accuracy Noise Dynamics Data models Convolutional neural networks Residual neural networks

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and Data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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Lyapunov-based event-triggered control for nonlinear plants subject to disturbances and transmission delays

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science China(Information sciences) 2020年第5期63卷 146-160页

作者： Xiaoda HU Hao YU Fei HAO The Seventh Research Division School of Automation Science and Electrical EngineeringBeihang University The Department of Electrical and Computer Engineering University of Alberta

This paper studies event-triggered control for disturbed nonlinear systems. A new dual-stage Lyapunov-based event-triggering condition is proposed to cope with the time-varying transmission *** the first stage, the ratio of Lyapunov function values at the last two triggering instants is *** based on the ratio, the corresponding threshold function is selected from two candidate forms. It is proved that the designed event-triggered control system is input-to-state practically stable with respect to the measurement errors and disturbances. Moreover, Zeno behavior is excluded successfully by calculating the lower bound of the minimum inter-event times. Finally, a simulation example is provided to show the feasibility and the effectiveness of the proposed approach.

关键词： event-triggered control nonlinear systems networked control systems disturbed systems transmission delays

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Long Short-Term Memory with Encoder-Decoder and Skill Optimization Algorithm based Weather Radar Echo Prediction 4

Long Short-Term Memory with Encoder-Decoder and Skill Optimi...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Ramijaanaki, S. Niveditha, V.R. Sree, G.V.N. Bhagya Rajababu, D. Maranan, R. Kaliappan, S. R.M.K College of Engineering and Technology Artificial Intelligence and Data Science Puduvoyal Tiruvallur district India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Semmancheri Chennai600119 India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

This research proposes a novel implementation of Long Short-Term Memory with Encoder-Decoder and Skill Optimization Algorithm (LSTM-ED-SOA) seeking to forecast Weather Radar Echoes. It presents a model that seeks to strengthen 'the weak' by introducing an optimization algorithm that combines the advantages of LSTM in handling sequential data and improves the performance of the predictions over time. Temporal dependencies are appropriately modeled with the encoder-decoder structure which is coupled with a skill optimization technique that enables the model to mitigate against capricious weather conditions. Forecasting models such as EPD, TrajGRU, and ConvLSTM as well as, and PredRNN put together show weaknesses when compared to LSTM-ED-SOA. Thus, it comes out convincingly that the model performs exceptionally achieving an accuracy of 99.8%, precision of 99.6%, and an F1 score of 99.7% way ahead of the best conventional models which were achieved accuracy of 85% at the very best. These results underscore the stability and effectiveness of the LSTM-ED-SOA system, for more advanced radars, wherein, it will greatly enhance the accuracy and timeliness of weather forecasting. © 2024 IEEE.

关键词： Weather forecasting

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Scaling the "memory Wall" for Multi-Dimensional Seismic Processing with Algebraic Compression on Cerebras CS-2 Systems 23

Scaling the "memory Wall" for Multi-Dimensional Seismic Proc...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Ltaief, Hatem Hong, Yuxi Wilson, Leighton Jacquelin, Mathias Ravasi, Matteo Keyes, David Elliot Extreme Computing Research Center Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Cerebras Systems Inc. SunnyvaleCA United States

ISBN: (纸本)9798400701092

We exploit the high memory bandwidth of AI-customized Cerebras CS-2 systems for seismic processing. By leveraging low-rank matrix approximation, we fit memory-hungry seismic applications onto memory-austere SRAM wafer-scale hardware, thus addressing a challenge arising in many wave-equation-based algorithms that rely on Multi-Dimensional Convolution (MDC) operators. Exploiting sparsity inherent in seismic data in the frequency domain, we implement embarrassingly parallel tile low-rank matrix-vector multiplications (TLR-MVM), which account for most of the elapsed time in MDC operations, to successfully solve the Multi-Dimensional Deconvolution (MDD) inverse problem. By reducing memory footprint along with arithmetic complexity, we fit a standard seismic benchmark dataset into the small local memories of Cerebras processing elements. Deploying TLR-MVM execution onto 48 CS-2 systems in support of MDD gives a sustained memory bandwidth of 92.58PB/s on 35, 784, 000 processing elements, a significant milestone that highlights the capabilities of AI-customized architectures to enable a new generation of seismic algorithms that will empower multiple technologies of our low-carbon future. © 2023 Owner/Author(s).

关键词： Energy efficiency

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Internet of Things based Liquified Petroleum Gas Leak Detection and Rapid Alert System 3

Internet of Things based Liquified Petroleum Gas Leak Detect...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Malik, Praveen Kumar Shaikh, Nilofar Lande, Jayapal Sahithi, Rajidi Sethi, Gaurav Singh, Rajesh School of Electronics and Electrical Engineering Lovely Professional University Punjab Jalandhar India Bvrit College of Engineering for Women Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Uttaranchal University Division of Research & Innvovation Dehradun India

ISBN: (纸本)9798350330724

Liquefied petroleum gas, or LPG, is widely utilised in homes, businesses, and industries. However, if not handled appropriately, LPG can offer a severe fire and explosion danger. As a result, it is critical to detect LPG leaks quickly and take adequate precautions to avoid mishaps. An Internet of Things (IoT)-based LPG leak monitoring and alarm system has been created to address this issue. The utilization of IoT technology has revolutionized the detection and alert mechanisms for LPG leaks. Traditionally, identifying and managing LPG leaks posed a significant challenge due to the need for human monitoring, high costs, and complicated installation processes. However, with the advent of IoT-based LPG leak detection systems, this problem has been addressed in a reliable and cost-effective manner. Our research focuses on implementing an IoT-based LPG leak detection and alert system that utilizes sensors, microcontrollers, and wireless communication to detect and alert users of any type of LPG leak. This system is highly dependable, easy to set up, and can inform users both remotely and through an in-house alarm. By deploying this system, we aim to mitigate the risks of tragic accidents, fires, and explosions caused by LPG leaks. © 2023 IEEE.

关键词： Internet of things

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