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Design of Drone Remote-Control System using Human Motion Recognition 5

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Design of Drone Remote-Control System using Human Motion Rec...

5th IEEE Eurasian Conference on Educational Innovation, ECEI 2022

作者： Chen, Shyh-Wei Lai, Yu-Chun Liu, Chia-Hui Kuo, Chin-Guo Tunghai University Department of Computer Science Taichung Taiwan Tunghai University Master Program of Digital Innovation Taichung Taiwan Chinese Culture University Department of Applied Mathematics Taipei Taiwan National Taiwan Normal University Department of Industrial Education Taipei Taiwan

ISBN: (纸本)9781665433181

At present, the most common and low-cost method to control the drone is to use a mobile phone with an APP. The operator takes his mobile phone screen as a human-machine interface, and at the same time controls and receives the images returned by the drone. The control mode can be divided into two symmetry modes according to how the operator views and knows the location of the drone: exocentric viewing and egocentric viewing mode. In the exocentric viewing mode, the operator directly visually confirms the position of the drone, while in the egocentric viewing mode, the drone is equipped with a camera and transmits the flight image to the operator in real-time. Since the drone cannot be far away from the operator in the exocentric viewing mode, the relative egocentric mode allows the operator to feel immersed in the environment to control the drone more accurately. Therefore, by adapting the egocentric viewing and using human motion recognition, an unmanned vehicle remote-control system is designed. The low-speed, small-scaled, nature-stability UAVs are ideally suited for the proposed system. The system was designed with a PID controller to improve the UAV's handling. The experimental results verified the feasibility of the UVA control system and it could stabilize the posture of the drone. © 2022 IEEE.

关键词： Drones

DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional

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arXiv 2023年

作者： Ou, Qi Tuo, Ping Li, Wenfei Wang, Xiaoxu Chen, Yixiao Zhang, Linfeng AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density functional theory (DFT) within the generalized gradient approximation (GGA) offers reliable prediction in terms of lattice constants and potential energy surface for halide perovskites, it severely underestimates the band gap due to the lack of non-local exact exchange term, which exists in computationally expensive hybrid functionals. In this work, a universal Deep Kohn-Sham (DeePKS) model based on neural network is trained so as to enable electronic structure calculations with the accuracy of hybrid functional HSE06 and the efficiency comparable to GGA functional, for a plethora of halide perovskites, i.e., ABX3 (A=FA, MA, Cs;B=Sn, Pb;X=Cl, Br, I). Forces, band gaps, and density of states (DOS) predicted by our DeePKS model for all aforementioned perovskites are in good agreement with the HSE06 results, with significantly improved efficiency. In addition, even though the spin-orbit coupling (SOC) effect has not been taken into consideration during the training process, DeePKS+SOC offers highly consistent band gap and DOS as compared to HSE06+SOC for Pb-containing systems. We believe such DeePKS model can be readily applied for an accurate yet efficient prediction of various properties for the family of halide perovskites. © 2023, CC BY.

关键词： Energy gap

Particle swarm optimization with local search for height-map surface reconstruction from point clouds in reverse engineering

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Neural Computing and Applications 2024年 1-20页

作者： Gálvez, Akemi Fister, Iztok Deb, Suash Fister, Iztok Iglesias, Andrés Dept. of Applied Mathematics and Computational Sciences E.T.S.I. Caminos Canales y Puertos University of Cantabria Avda. de los Castros s/n Santander39005 Spain Faculty of Pharmaceutical Sciences Toho University 2-2-1 Miyama Funabashi274-8510 Japan Faculty of Electrical Engineering and Computer Science University of Maribor Koroska cesta 34 Maribor2000 Slovenia IT & Educational Consultant Ranchi India Distinguished Professorial Associate Decision Sciences and Modeling Program Victoria University Melbourne Australia

Surface reconstruction is a classical process in industrial engineering and manufacturing, particularly in reverse engineering, where the goal is to obtain a digital model from a physical object. For that purpose, the real object is typically scanned or digitized and the resulting point cloud is then fitted to mathematical surfaces through numerical optimization. The choice of the approximating functions is crucial for the accuracy of the process. Real-world objects such as manufactured workpieces often require complex nonlinear approximating functions, which are not well suited for standard numerical methods. In a previous paper presented at the ISMSI 2023 conference, we addressed this issue by using manually selected approximation functions via optimization through the cuckoo search algorithm with Lévy flights. Building upon that work, this paper presents an enhanced and extended method for surface reconstruction by using height-map surfaces obtained through a combination of exponential, polynomial and logarithmic functions. A feasible approach for this goal is to consider continuous bivariate distribution functions, which ensures consistent models along with good mathematical properties for the output shapes, such as smoothness and integrability. However, this approach leads to a difficult multivariate, constrained, multimodal continuous nonlinear optimization problem. To tackle this issue, we apply particle swarm optimization, a popular swarm intelligence technique for continuous optimization. The method is hybridized with a local search procedure for further improvement of the solutions and applied to a benchmark of 15 illustrative examples of point clouds fitted to different surface models. The performance of the method is analyzed through several computational experiments. The numerical and graphical results show that the method is able to recover the shape of the point clouds accurately and automatically. Furthermore, our approach outperforms other alternati

关键词： Particle swarm optimization (PSO)

Inferring System and Optimal Control Parameters of Closed-Loop Systems from Partial Observations

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arXiv 2025年

作者： Geadah, Victor Arbelaiz, Juncal Ritz, Harrison Daw, Nathaniel D. Cohen, Jonathan D. Pillow, Jonathan W. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ United States Princeton Neuroscience Institute Princeton University PrincetonNJ United States

We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time horizons in a partially observed setting, where the state is observed noisily. To recover closed-loop system parameters, we develop inference methods based on probabilistic state-space model (SSM) techniques. First, we show that the system parameters exhibit non-identifiability in the infinite-horizon from closed-loop measurements, and we provide exact and numerical methods to disentangle the parameters. Second, to improve parameter identifiability, we show that we can further enhance recovery by either (1) incorporating additional partial measurements of the control signals or (2) moving to the finite-horizon setting. We further illustrate the performance of our methodology through numerical examples. © 2025, CC BY.

关键词： Stochastic systems

Comparison of the Effectiveness of Machine Learning Techniques for the Number of Road Traffic Injuries

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Comparison of the Effectiveness of Machine Learning Techniqu...

International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON)

作者： Wirawan Puttamat Wikanda Phaphan Anuchit Jitpattanakul Chainarong Pianpailoon Sakorn Mekruksavanich Department of Mathematics Faculty of Education Chaiyaphum Rajabhat University Chaiyaphum Thailand Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Research Group in Statistical Learning and Inference Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Curriculum and Teaching Program (Mathematics) Faculty of Education Sakon Nakhon Rajabhat University Sakon Nakhon Thailand Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand

This article aims to compare forecasting techniques with machine learning techniques for predicting the number of people injured in road accidents using data from the Injury Information Collaboration Center, Department of Disease Control, Ministry of Public Health Outpatient Files (OPD) spanning the years 2018 to 2022. The four machine-learning techniques examined in this study include Decision Tree Regression, Random Forest Regression, Support Vector Regression, and Multiple Linear Regression. The data analysis was performed using the R programming language. The results revealed that the Decision Tree Regression technique yielded the most accurate predictions for the number of road traffic injuries, as evidenced by its lowest values of MAE, MSE, and RMSE.

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THE TSENG'S EXTRAGRADIENT METHOD FOR SEMISTRICTLY QUASIMONOTONE VARIATIONAL INEQUALITIES

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Journal of Applied and Numerical Optimization

Journal of applied and Numerical Optimization 2022年第2期4卷 203-214页

作者： Ur Rehman, Habib Özdemir, Murat Karahan, Ibrahim Wairojjana, Nopparat Departments of Mathematics King Mongkut's University of Technology Thonburi Bangkok10140 Thailand Department of Mathematics Faculty of Science Ataturk University Erzurum25030 Turkey Department of Mathematics Faculty of Science Erzurum Technical University Erzurum25050 Turkey Applied Mathematics Program Faculty of Science and Technology Valaya Alongkorn Rajabhat University under the Royal Patronage Pathumthani13180 Thailand

In this paper, we investigate the weak convergence of an iterative method for solving classical variational inequalities problems with semistrictly quasimonotone and Lipschitz-continuous mappings in real Hilbert space. The proposed method is based on Tseng's extragradient method and uses a set stepsize rule that is dependent on the Lipschitz constant as well as a simple self-adaptive stepsize rule that is independent of the Lipschitz constant. We proved a weak convergence theorem for our method without requiring any additional projections or the knowledge of the Lipschitz constant of the involved mapping. Finally, we offer some numerical experiments that demonstrate the efficiency and benefits of the proposed method. © 2022 Journal of applied and Numerical Optimization.

关键词： Iterative methods

Learning High-Dimensional McKean-Vlasov Forward-Backward Stochastic Differential Equations with General Distribution Dependence

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arXiv 2022年

作者： Han, Jiequn Hu, Ruimeng Long, Jihao Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Department of Mathematics Department of Statistics and Applied Probability University of California Santa BarbaraCA93106-3080 United States The Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544-1000 United States

One of the core problems in mean-field control and mean-field games is to solve the corresponding McKean-Vlasov forward-backward stochastic differential equations (MV-FBSDEs). Most existing methods are tailored to special cases in which the mean-field interaction only depends on expectation or other moments and thus is inadequate to solve problems when the mean-field interaction has full distribution dependence. In this paper, we propose a novel deep learning method for computing MV-FBSDEs with a general form of mean-field interactions. Specifically, built on fictitious play, we recast the problem into repeatedly solving standard FBSDEs with explicit coefficient functions. These coefficient functions are used to approximate the MV-FBSDEs’ model coefficients with full distribution dependence, and are updated by solving another supervising learning problem using training data simulated from the last iteration’s FBSDE solutions. We use deep neural networks to solve standard BSDEs and approximate coefficient functions in order to solve high-dimensional MV-FBSDEs. Under proper assumptions on the learned functions, we prove that the convergence of the proposed method is free of the curse of dimensionality (CoD) by using a class of integral probability metrics (IPMs) previously developed in [Han, Hu and Long, arXiv:2104.12036]. The proved theorem shows the advantage of the method in high dimensions. We present the numerical performance in high-dimensional MV-FBSDE problems, including a mean-field game example of the well-known Cucker-Smale model whose cost depends on the full distribution of the forward *** Codes 65C30, 68T07, 49N80, 68Q25 © 2022, CC BY.

关键词： Stochastic systems

On The Mechanism of Nucleation in Pulsed-laser Quenched Si Films on SiO 2

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MRS Online Proceedings Library 2021年第1期1245卷 1-6页

作者： Y. Deng Q. Hu U. J. Chung A. M. Chitu A. B. Limanov S. James Im Program in Materials Science and Engineering Department of Applied Physics and Applied Mathematics Columbia University New York USA

We have investigated the solid nucleation mechanism in laser-quenched Si films on SiO2. Previously neglected experimental steps, consisting of BHF-etching and irradiation in vacuum, were implemented to reduce potential extrinsic influences. The resulting experimental findings and computational analysis lead us to conclude that solid nucleation consistently takes place heterogeneously at, and only at, the bottom liquid Si-SiO2 interface.

关键词： Si thin film nucleation & growth

Stochastic Dynamics and Probability Analysis for a Generalized Epidemic Model with Environmental Noise

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arXiv 2024年

作者： Boukanjime, Brahim Maama, Mohamed Laboratory of Mathematical Modeling and Economic Calculations Hassan First University of Settat Morocco Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Saudi Arabia

In this paper we consider a stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with a generalized incidence function. Using the Lyapunov method, we establish the existence and uniqueness of a global positive solution to the model, ensuring that it remains well-defined over time. Through the application of Young’s inequality and Chebyshev’s inequality, we demonstrate the concepts of stochastic ultimate boundedness and stochastic permanence, providing insights into the long-term behavior of the epidemic dynamics under random perturbations. Furthermore, we derive conditions for stochastic extinction, which describes scenarios where the epidemic may eventually die out, and V-geometric ergodicity, which indicates the rate at which the system’s state converges to its equilibrium. Finally, we perform numerical simulations to verify our theoretical results and assess the model’s behavior under different parameters. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models