Article
Mathematics
Salma Iqbal, Naveed Yaqoob, Muhammad Gulistan
Summary: The research presents an interactive method for solving nonlinear fractional programming problems using the linear Diophantine fuzzy set notion. The method involves solving a max-min problem using Zimmermann's min operator method when the decision maker defines the degree of a level sets. By updating the degree of a, the decision maker can be solved from a set of a-cut optimal solutions based on the membership and non-membership functions. The approach demonstrates the reduction of a Diophantine fuzzy nonlinear programming problem to a crisp multi-objective nonlinear fractional programming problem, which can then be solved using any suitable algorithm.
Article
Automation & Control Systems
M. Sarwar Sindhu, Tabasam Rashid, Agha Kashif
Summary: This study introduces bipolar picture fuzzy sets (BP(c)FSs) and develops aggregation operators and a multiple criteria decision-making approach to deal with vague data, demonstrating the authenticity and efficacy of the proposed method through examples and analysis.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Physics, Multidisciplinary
S. T. Chui, Zhifang Lin, Zian Ji
Summary: We demonstrate the concept of resonance in noise through the example of current induced by an external electromagnetic field and noise current in split ring resonators. Both the signal and noise show resonances at the same frequencies, but their ratio varies with frequency and external driving field. This unexpected result suggests that the signal to noise ratio is not solely determined by the resonance frequency, highlighting the need for further research to improve device design.
Article
Computer Science, Artificial Intelligence
Mostafa Rostaghi, Mohammad Mahdi Khatibi, Mohammad Reza Ashory, Hamed Azami
Summary: Entropy is a powerful tool for nonlinear analysis of time series, but it is sensitive to parameters and can be influenced by noise. To address these issues, we developed fuzzy dispersion entropy, which combines fuzzy membership functions and Shannon entropy. We demonstrated the advantages of fuzzy dispersion entropy over traditional dispersion entropy in detecting the dynamical variability of signals. The results showed that fuzzy dispersion entropy has lower sensitivity to noise and signal length. It was also successfully applied to various real-world applications.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Sahar Cherif, Nesrine Baklouti, Hani Hagras, Adel M. Alimi
Summary: This article proposes three new interval type-2 fuzzy similarity measures and proves their common properties. The experiments show that these measures are resilient to high levels of uncertainty noise and can overcome the drawbacks of existing similarity measures. The application of these measures in clustering algorithms also achieves good results.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Izaz Ullah Khan, Muhammad Aftab
Summary: An adaptive fuzzy dynamic programming technique is proposed to solve linear programming problems with fuzzy constraints. The technique can handle the case where both the left and right hand sides of the constraints are fuzzy. It ensures the existence of a feasible solution for fuzzy linear programming problems.
Article
Thermodynamics
Esra Ilbahar, Cengiz Kahraman, Selcuk Cebi
Summary: This study identified the most suitable locations for waste-to-energy power plants in Central Anatolia Region of Turkey using a fuzzy linear programming model, suggesting Ankara, Konya, Kayseri, Eskisehir, Sivas, and Aksaray as the most suitable cities for investment.
Article
Mathematics
Ranka Gojkovic, Goran Duric, Danijela Tadic, Snezana Nestic, Aleksandar Aleksic
Summary: The research proposes a hybrid decision-making model for evaluating and selecting quality methods to improve manufacturing reliability. Utilizing FMEA and TIFNs, the model addresses uncertainties in risk factors, values, method applicability, and implementation costs. A genetic algorithm is used to solve the quality method selection problem, showing suitability for SMEs in the process industry.
Article
Engineering, Electrical & Electronic
Kosuke Suzuoki, Daisuke Hisano, Sho Shibita, Kazuki Maruta, Akihiro Maruta
Summary: This study proposes an optimal quantizer based on the Lloyd-Max algorithm to improve the effect of power ratio increase between multiplexed ONUs in a PD-NOMA-PON system.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2021)
Article
Chemistry, Analytical
Yan Dai, Dan Liu, Qingrong Hu, Xiaoli Yu
Summary: This paper proposes a target detection algorithm using convolutional neural network under low signal-to-noise ratio, which processes graphically expressed range time series signals. The algorithm first processes the two-dimensional echo signal graphically, and then detects the graphical echo signal using the improved convolutional neural network. Simulation results show that the proposed method has a higher target detection probability compared to the multi-pulse accumulation detection method, indicating its effectiveness.
Article
Computer Science, Theory & Methods
Juan Carlos Diaz-Moreno, Jesus Medina, Jose R. Portillo
Summary: This paper proposes an equivalent representation of multi-adjoint logic programs using hypergraphs, which increases the level and flexibility of termination results in computing the least model of fuzzy logic programs.
FUZZY SETS AND SYSTEMS
(2022)
Article
Multidisciplinary Sciences
Rodion Kononchuk, Jizhe Cai, Fred Ellis, Ramathasan Thevamaran, Tsampikos Kottos
Summary: The study investigates an electromechanical accelerometer based on exceptional points (EP) and parity-time symmetry, demonstrating that the enhanced technical noise can be overcome by the enhanced responsivity to accelerations. By utilizing detuning from a transmission peak degeneracy (TPD) as a measure of sensitivity, the noise due to eigenbasis collapse can be mitigated, resulting in a threefold signal-to-noise-ratio enhancement compared with other configurations operating away from the TPD.
Article
Engineering, Environmental
Qiusheng Song, Peng Jiang, Song Zheng
Summary: This paper proposes a safety assessment method for chemical plant production process that comprehensively considers the safety influencing elements of human factors. The method combines analytic hierarchy process and cloud model for evaluation, and simulation results show that it is reliable, practical, and scientific.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2021)
Article
Computer Science, Information Systems
Isha Singh, Om Prakash Verma
Summary: This paper introduces a two-step fuzzy filter to remove impulse noise from a color image sequence in RGB color space. The filter recognizes and corrects pixels corrupted by impulse noise, resulting in good alignment between noise removal and structure conservation. The filter considers spatial, temporal, and color information, demonstrating effectiveness qualitatively and quantitatively in experimental results with various color image sequences.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Optics
L. A. N. X. I. N. Zhu, Y. U. W. E. N. Cao, H. U. I. M. I. N. Huang, Y. A. N. J. U. N. Chen, W. E. N. B. O. Wang, X. I. A. N. G. D. O. N. G. Ma, Z. H. E. N. G. B. I. N. LI
Summary: This letter analyzes the scale factor nonlinearity error introduced by harmonic distortion in open-loop interferometric fiber optic gyroscopes, and proposes an effective and simple compensation method. Experimental results show that with this method, the scale factor nonlinear error is suppressed to 2.5 ppm within the range of -300 to +300 degrees/s, which is 33 times lower than before compensation.
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
Soung Sub Lee
Summary: This study proposed a satellite constellation method that utilizes machine learning and customized repeating ground track orbits to optimize satellite revisit performance for each target.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Jian Wang, Xiuying Zhan, Yuping Yan, Guosheng Zhao
Summary: This paper proposes a method of user recruitment and adaptation degree improvement via community collaboration to solve the task allocation problem in sparse mobile crowdsensing. By matching social relationships and perception task characteristics, the entire perceptual map can be accurately inferred.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, Ken Chen
Summary: This paper investigates how to reconfigure existing compliance controllers for new assembly objects with different geometric features. By using the proposed Equivalent Theory of Compliance Law (ETCL) and Weighted Dimensional Policy Distillation (WDPD) method, the learning cost can be reduced and better control performance can be achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Zhihao Xu, Zhiqiang Lv, Benjia Chu, Zhaoyu Sheng, Jianbo Li
Summary: Predicting future urban health status is crucial for identifying urban diseases and planning cities. By applying an improved meta-analysis approach and considering the complexity of cities as systems, this study selects eight urban factors and explores suitable prediction methods for these factors.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Yulong Ye, Qiuzhen Lin, Ka-Chun Wong, Jianqiang Li, Zhong Ming, Carlos A. Coello Coello
Summary: This paper proposes a localized decomposition evolutionary algorithm (LDEA) to tackle imbalanced multi-objective optimization problems (MOPs). LDEA assigns a local region for each subproblem using a localized decomposition method and restricts the solution update within the region to maintain diversity. It also speeds up convergence by evolving only the best-associated solution in each subproblem while balancing the population's diversity.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Longxin Zhang, Jingsheng Chen, Jianguo Chen, Zhicheng Wen, Xusheng Zhou
Summary: This study proposes a lightweight PCB image defect detection network (LDD-Net) that achieves high accuracy by designing a novel lightweight feature extraction network, multi-scale aggregation network, and lightweight decoupling head. Experimental results show that LDD-Net outperforms state-of-the-art models in terms of accuracy, computation, and detection speed, making it suitable for edge systems or resource-constrained embedded devices.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Kemal Ucak, Gulay Oke Gunel
Summary: This paper introduces a novel adaptive stable backstepping controller based on support vector regression for nonlinear dynamical systems. The controller utilizes SVR to identify the dynamics of the nonlinear system and integrates stable BSC behavior. The experimental results demonstrate successful control performance for both nonlinear systems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Article
Automation & Control Systems
Dexuan Zou, Mengdi Li, Haibin Ouyang
Summary: In this study, a photovoltaic thermal collector is integrated into a combined cooling, heating, and power system to reduce primary energy consumption, operation cost, and carbon dioxide emission. By applying a novel genetic algorithm and constraint handling approach, it is found that the CCHP scenarios with PV/T are more efficient and achieve the lowest energy consumption.
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
Zahra Ramezanpoor, Adel Ghazikhani, Ghasem Sadeghi Bajestani
Summary: Time series analysis is a method used to analyze phenomena with temporal measurements. Visibility graphs are a technique for representing and analyzing time series, particularly when dealing with rotations in the polar plane. This research proposes a visibility graph algorithm that efficiently handles biological time series with rotation in the polar plane. Experimental results demonstrate the effectiveness of the proposed algorithm in both synthetic and real world time series.
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
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
Jicong Duan, Xibei Yang, Shang Gao, Hualong Yu
Summary: The study proposes a novel partition-based imbalanced multi-label learning algorithm, MLHC, which divides the original label space into disconnected subspaces using hierarchical clustering. It successfully tackles the class imbalance problem in multi-label data and outperforms other class imbalance multi-label learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
Review
Automation & Control Systems
Qing Qin, Yuanyuan Chen
Summary: This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2024)
