Article
Automation & Control Systems
Chinu Singla, Raman Maini, Munish Kumar
Summary: This study investigates the field of age, gender, and handedness prediction through handwriting analysis, offering valuable insights into its applications in forensics, psychology, and education. A comprehensive survey is conducted on Indic and non-Indic scripts, highlighting research gaps and providing a roadmap for future advancements. The study concludes that non-Indic scripts achieve higher accuracy compared to Indic scripts, and focuses on providing a catalog of publicly accessible datasets for further research in this area.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Abhinav Pandey, Litton Bhandari, Vidit Gaur
Summary: This research proposes a novel model-agnostic framework based on genetic algorithms to identify and optimize the set of coefficients of the constitutive equations of engineering materials. The framework demonstrates solution convergence, scalability, and high explainability for a wide range of engineering materials. The experimental validation shows that the proposed framework outperforms commercially available software in terms of optimization efficiency.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
ChunLi Li, Qintai Hu, Shuping Zhao, Jigang Wu, Jianbin Xiong
Summary: Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial. However, the nonlinear and non-stationary vibration signals generated in harsh environments pose challenges in distinguishing fault signals from normal ones. This paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model to address these challenges. The effectiveness of the proposed method in fault diagnosis is demonstrated through validation and comparative analysis with a published method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Carmen Bisogni, Lucia Cimmino, Michele Nappi, Toni Pannese, Chiara Pero
Summary: This paper presents a gait-based emotion recognition method that does not rely on facial cues, achieving competitive performance on small and unbalanced datasets. The proposed approach utilizes advanced deep learning architecture and achieves high recognition and accuracy rates.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Subhashis Nandy
Summary: This research focuses on the design and stability analysis of nonlinear controllers for an electrically driven marine cycloidal propeller, along with estimating various parameters using the Extended Kalman Filter. The controller is defined using an efficient physics-based model and is able to accurately process multiple control signals. The robustness of the controller is assessed using Monte Carlo simulation, and its performance is evaluated through validation investigations.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Weilian Liu, Xinyi He, Xiaodi Li
Summary: This paper investigates the problem of global exponential stability for nonlinear delay impulsive systems. By extending the traditional comparison principle and estimating the effects of delay on continuous and discrete dynamics, the internal relationship between delays, parameters of impulsive control, and continuous dynamics is revealed. Sufficient criteria for global exponential stability are obtained, quantitatively demonstrating the beneficial influences of delays on the system performance.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Automation & Control Systems
Feng Li, Zhenghao Ni, Lei Su, Jianwei Xia, Hao Shen
Summary: This paper addresses the problem of finite-region passive control for 2-D Markov jump Roesser systems, considering the partial statistical information issues on Markov parameters and transition probabilities. A 2-D hidden Markov model with partial statistical information is established to model this situation. The goal is to design a controller based on the 2-D hidden Markov model that ensures finite-time boundedness of both horizontal and vertical states of the 2-D Markov jump Roesser systems, while meeting a passive performance criterion. By employing the Lyapunov function method, criteria for the finite-region boundedness of 2-D Markov jump Roesser systems are developed, and a design method for the asynchronous controller based on the 2-D hidden Markov model is presented. The effectiveness of the proposed design method is validated through an illustrative example.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Automation & Control Systems
Chunmei Zhang, Huiling Chen, Qin Xu, Yuli Feng, Ran Li
Summary: This article discusses a class of stochastic hybrid delayed coupled systems with multiple weights, and derives several conditions for asymptotic synchronization and topology identification of the systems based on Kirchhoff's Matrix-Tree Theorem and Lyapunov stability theory.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Automation & Control Systems
Everaldo de Mello Bonotto, Piotr Kalita
Summary: We propose new criteria for the existence of global attractors for problems with state-dependent impulses that are more general than those previously known. Our results are applicable to both nonunique and unique solutions, and we provide collective versions of the criteria that demonstrate the upper-semicontinuity of global attractors under perturbation. The theory is illustrated through examples of ODEs and PDEs.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
Article
Automation & Control Systems
Jiao Zhu, Sugen Chen, Yufei Liu, Cong Hu
Summary: This study proposes a novel energy-based structural least squares twin support vector clustering algorithm (ESLSTWSVC), which improves clustering performance and efficiency by introducing within-class covariance matrix and solving system of linear equations.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yixiong Feng, Zetian Zhao, Bingtao Hu, Yong Wang, Hengyuan Si, Zhaoxi Hong, Jianrong Tan
Summary: This paper proposes an innovative method for condition monitoring of nuclear turbines. By redesigning time augmented matrices and building a dynamic auto-regressive model, this method can accurately monitor the working condition of nuclear turbines, thus enhancing the safety and reliability of nuclear power plants.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Qiaosheng Pan, Yifang Zhang, Xiaozhu Chen, Quan Wang, Qiangxian Huang
Summary: A resonant piezoelectric rotary motor using parallel moving gears mechanism has been proposed and tested, showing high power output and efficiency.
Article
Automation & Control Systems
Hao Chen, Jieyu Zhao, Kangxin Chen, Yu Chen
Summary: This paper proposes a self-supervised spherical vector network that learns the orientation perception from 3D point clouds through experience. The proposed network utilizes density-aware adaptive sampling and spherical convolutional vector layers for handling and extraction. Experimental results demonstrate the effectiveness of the method in canonical orientation estimation.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhaohui Jiang, Jinshi Liu, Zhiwen Chen, Weichao Luo, Chaobo Zhang, Weihua Gui
Summary: In this paper, a method for estimating the overall particle size distribution (PSD) based on mechanistic modeling and local-global fused prediction networks is proposed. The method can accurately predict the PSD in harsh production environments with limited detection conditions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
S. Lyaqini, A. Hadri, A. Ellahyani, M. Nachaoui
Summary: In this paper, an improved version of Twin SVM using a non-smooth optimization method is proposed. The proposed approach solves the problem of limited handle Gaussian noise, exaggerated influence of outliers and inability to handle unbalanced data in Twin SVM. By transforming the two-constraint optimization models into an unconstrained non-smooth optimization problem and using the primal dual method to solve it, the proposed approach demonstrates its effectiveness and applicability through experiments on different datasets.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dmitry Yudin, Nikita Zakharenko, Artem Smetanin, Roman Filonov, Margarita Kichik, Vladislav Kuznetsov, Dmitry Larichev, Evgeny Gudov, Semen Budennyy, Aleksandr Panov
Summary: This research aims to improve waste recycling efficiency through deep learning applications and reduce carbon emissions from computing devices. A diverse dataset is developed for training and evaluating the performance of neural networks in waste recognition, classification, and segmentation tasks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kunpeng Zhang, Jikang Gao, Zongqi Xu, Hui Yang, Ming Jiang, Rui Liu
Summary: A improved dynamic programming model is proposed in this paper for joint operation optimization of virtual coupling of heavy-haul trains. By simultaneously optimizing the headway and energy savings, as well as performing locomotive engineering advisory analysis, significant improvements in train performance can be achieved.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Jih-Chang Wang, Ting-Yu Chen
Summary: This research aims to create a C-IF VIKOR decision-support method to handle multiple-criteria compromise solutions with circular intuitionistic fuzzy (C-IF) uncertainties. The study focuses on enhancing the augmented scoring function and Chebyshev distance metric in C-IF surroundings to determine the finest compromise solution.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Fatemeh Chahkoutahi, Mehdi Khashei
Summary: The classification rate is the most important factor in selecting an appropriate classification approach. In this paper, the influence of different cost/loss functions on the classification rate of different classifiers is compared, and empirical results show that cost/loss functions significantly affect the classification rate.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yun Zhu, Kangkang Zhang, Yucai Zhu, Pengfei Jiang, Jinming Zhou
Summary: In this study, a three-term Dynamic Matrix Control (DMC) algorithm using quadratic programming is developed and compared with the traditional two-term DMC algorithm. Simulation studies and real-life tests show that the three-term DMC algorithm outperforms the two-term DMC algorithm in control effectiveness.
CONTROL ENGINEERING PRACTICE
(2024)
