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Dynamics of the competition between two languages

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SeMA Journal 2020年第4期77卷 351-373页

作者： Tchendjeu, A. E. Tchahou Bowong, S. Tchitnga, R. Fotsin, H.B. Department of Computer Engineering National Higher Polytechnic Institute University of Bamenda PO Box 39 Bambili Cameroon Laboratory of Condensed Matters Electronics and Signal Processing (LAMACETS) Department of Physics Faculty of Science University of Dschang PO Box 63 Dschang Cameroon Research Group on Experimental and Applied Physics for Sustainable Development (EAPhySuD) Department of Physics Faculty of Science University of Dschang PO Box 63 Dschang Cameroon Laboratory of Applied Mathematics Department of Mathematics and Computer Science Faculty of Science University of Douala PO Box 24157 Douala Cameroon UMI 209 IRD/UPMC UMMISCO Bondy France LIRIMA Project Team GRIMCAPE Yaounde Cameroon The African Center of Excellence in Information and Communication Technologies (CETIC) University of Yaounde 1 Yaounde Cameroon Institute of Surface Chemistry and Catalysis University of Ulm Albert-Einstein-Allee 47 Ulm 89081 Germany

This paper reports a new kind of mathematical model for language competition dynamics using compartmental epidemiological modeling approach. The model describes the competition between two languages, say the predominant one called language 1, and the less spoken language 2, both in the same community. We distinguish three groups of population building the concerned community: one majority speaking language 1, one minority speaking language 2 and a last minority speaking neither language 1 nor language 2. The study of the proposed model includes an analysis of the evolution of the number of speakers over time. This model predicts that language 2 can inevitably disappear if the threshold parameter R is less than one. We show that our model is well-posed mathematically and linguistically. We also show that the model has basically two linguistic equilibrium points. A monolingual equilibrium point that corresponds to the case where only one language is spoken: some individuals speak only the language 1 and some others do speak neither language 1, nor language 2. A bilingual equilibrium point which corresponds to the case where all the languages are spoken: some individuals speak both languages 1 and 2, some others speak only language 1, while a third group does speak only language 2, and the last group speaks none of the two languages. In this bilingual equilibrium point, both languages coexist. Depending on the threshold parameter, we demonstrate the stability of these equilibria. The model presents on one hand, a direct bifurcation phenomenon, in which we have a stable equilibrium point without bilingual speakers when the associated basic reproduction number is less than one. On the other hand, it presents a stable bilingual equilibrium point when the number of associated basic reproductions is greater than one. The analysis of the overall sensitivity of the model is performed and the impact of the system parameters on the basic reproduction number was performed using

关键词： Bilingualism Compartmental modeling Dynamical system Language competition Stability

Automatic Modulation Classification of Frequency-Hopping signals Using High-Dimensional Phase Diagrams

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Automatic Modulation Classification of Frequency-Hopping Sig...

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Qibo Chen Kun Yan Hsiao-Chun Wu Shih Yu Chang Xiao Yan Yiyan Wu Haonan Chang Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Department of Applied Data Science San Jose State University San Jose CA USA School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu P. R. China Communications Research Centre Canada Ottawa Canada Haonan Chang is with Xi'an University Xi'an P. R. China

Automatic modulation recognition for frequency-hopping (FH) signals remains very challenging to researchers due to the signals' time-varying spectral characteristics. In this work, a novel robust automatic modulation recognition scheme is investigated for FH signals using the phase-space topological features represented by the embedded phase diagrams. As such embedded phase diagrams are often high-dimensional, it is necessary to formulate the phase-space features as tensors. In the training process, the phase-space tensor features will be utilized to establish the regression models as linear encoders for the individual modulations. The aforementioned linear encoders are constructed using the support vector machine (SVM); the phase-space feature-tensors of the training signals of all modulations will be projected by their corresponding regression models (or linearly encoded) to produce the representative code-vectors, respectively. In the test stage, the phase-space feature-tensor produced from a test signal will be projected by each individual trained regression model (or linearly encoded) to generate the respective code-vectors. Then, the code-vectors resulting from the test stage will be compared with the representative code-vectors to find which modulation will lead to the smallest Euclidean distance in between and such a modulation will be picked as the modulation type of the test signal. Monte Carlo simulation results have demonstrated that the average recognition accuracy of our proposed new approach is more than 90% when the signal-to-noise ratio is no less than 0 dB for additive white Gaussian noise.

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DOUBLE PHASE OBSTACLE PROBLEMS WITH MULTIVALUED CONVECTION AND MIXED BOUNDARY VALUE CONDITIONS

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arXiv 2021年

作者： Zeng, Shengda Radulescu, Vicent Iu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Department of Mathematics Nanjing University Jiangsu Nanjing210093 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Faculty of Applied Mathematics AGH University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania China-Romania Research Center in Applied Mathematics Craiova Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper, we consider a mixed boundary value problem with a double phase partial differential operator, an obstacle effect and a multivalued reaction convection term. Under very general assumptions, an existence theorem for the mixed boundary value problem under consideration is proved by using a surjectivity theorem for multivalued pseudomonotone operators together with the approximation method of Moreau-Yosida. Then, we introduce a family of the approximating problems without constraints corresponding to the mixed boundary value problem. Denoting by S the solution set of the mixed boundary value problem and by Sn the solution sets of the approximating problems, we establish the following convergence relation ∅ =6 w- lim sup Sn = s- lim sup Sn ⊂ S, n→∞ n→∞ where w-lim supn→∞ Sn and s-lim supn→∞ Sn stand for the weak and the strong Kuratowski upper limit of Sn, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Mathematical operators

Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation

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arXiv 2022年

作者： Zhao, Yue Zhang, Lingming Liu, Yang Meng, Deyu Cui, Zhiming Gao, Chenqiang Gao, Xinbo Lian, Chunfeng Shen, Dinggang Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China The Chongqing Key Laboratory of Signal and Information Processing Chongqing400065 China Department of Orthodontics Stomatological Hospital of Chongqing Medical University Chongqing401147 China Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing401147 China Faculty of Information Technology Macau University of Science and Technology China School of Mathematics and Statistics Xi'an Jiaotong University Xian710049 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China The School of Computer Science The University of Hong Kong 999077 Hong Kong School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation. Github: https://***/ZhangLingMing1/TSGCNet. © 2022, CC BY.

关键词： Scanning

Novel deep hybrid model for electricity price prediction based on dual decomposition

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applied Energy 2025年 395卷

作者： Sujan Ghimire Thong Nguyen-Huy Ravinesh C. Deo David Casillas-Pérez A.A. Masrur Ahmed Sancho Salcedo-Sanz Artificial Intelligence Applications Laboratory School of Mathematics Physics and Computing University of Southern Queensland Springfield QLD 4300 Australia Centre for Applied Climate Sciences University of Southern Queensland Toowoomba 4350 QLD Australia Faculty of Information Technology Thanh Do University Kim Chung Hoai Duc Ha Noi 100000 Viet Nam Department of Signal Processing and Communications Universidad Rey Juan Carlos Fuenlabrada 28942 Madrid Spain Science Economics and Insights Division Department of Climate Change Energy the Environment and Water NSW Government Sydney 2141 New South Wales Australia Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares 28805 Madrid Spain

Electricity price ( E P ) forecasting is vital for effective market operation, strategic planning, and risk management in deregulated energy systems. However, the inherent volatility and complexity of electricity prices, shaped by demand supply dynamics, weather variability, and regulatory interventions, pose substantial challenges to accurate prediction. This study introduces a novel hybrid framework designed to improve forecasting accuracy by leveraging both signal decomposition and deep learning techniques. Specifically, the method integrates Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) for noise reduction and feature extraction, followed by a Multi Resolution Convolution (MRC) layer and a Bidirectional Long Short Term Memory (BiLSTM) network to capture multiscale temporal patterns in electricity price data. The model is applied to half hourly electricity price data from South Australia spanning January 2018 to December 2022. Its performance is benchmarked against a suite of traditional and hybrid models using a comprehensive set of twelve evaluation metrics. The results reveal that the proposed hybrid model consistently outperforms all baselines across seasons and forecast horizons. Notably, during the spring period, it achieved a Normalized Root Mean Square Error of ≈ 4.87 % , a Mean Absolute Percentage Error of ≈ 12.09 % , and a Global Performance Index of ≈ 3.22 . These improvements demonstrate the model’s ability to effectively handle the non-linear and nonstationary nature of E P . Overall, the findings underscore the potential of combining advanced decomposition methods with deep learning architectures to deliver more accurate and robust E P forecasts, thereby offering valuable support for decision making in complex and evolving energy markets.

关键词： 0000 1111 0000 1111 Deep learning Convolutional neural network Variational mode decomposition Empirical wavelet transform Residual connection Bayesian optimization

Application of fractional calculus for impact oscillator modeling

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Application of fractional calculus for impact oscillator mod...

2019 International Conference on Intelligent Computing, Automation and Systems, ICICAS 2019

作者： Rekhviashvili, Sergo Pskhu, Arsen Potapov, A. Feng, Tianhua Narozhnov, Viktor Department of Theoretical and Mathematical Physics Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia JNU-IREE RAS Joint Laboratory of Information Techniques and Fractal Signal Processing Jinan University Guangzhou China Department of Fractional Calculus Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia Institute of Radioengineering and Electronics RAS Moscow Russia Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Laboratory of Measurement Automation Institute of Applied Mathematics and Automation KBSC RAS Nalchik Russia

ISBN: (纸本)9781728161068

A new theoretical model for describing an impact oscillator that is based on the use of a fractional derivative is constructed. The acoustic signals that occur when the probe strikes the surface of metals are experimentally investigated. It is shown that acoustic signals can be used to diagnose elastic properties of materials. © 2019 IEEE.

关键词： Energy dissipation

DOUBLE PHASE IMPLICIT OBSTACLE PROBLEMS WITH CONVECTION AND MULTIVALUED MIXED BOUNDARY VALUE CONDITIONS

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arXiv 2021年

作者： Zeng, Shengda Rǎdulescu, Vicenţiu D. Winkert, Patrick Guangxi Colleges Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Guangxi Yulin537000 China Jagiellonian University in Krakow Faculty of Mathematics and Computer Science ul. Lojasiewicza 6 Krakow30-348 Poland Faculty of Applied Mathematics Agh University of Science and Technology Kraków30-059 Poland Department of Mathematics University of Craiova Craiova200585 Romania Technische Universität Berlin Institut für Mathematik Straße des 17. Juni 136 Berlin10623 Germany

In this paper we consider a mixed boundary value problem with a nonhomogeneous, nonlinear differential operator (called double phase operator), a nonlinear convection term (a reaction term depending on the gradient), three multivalued terms and an implicit obstacle constraint. Under very general assumptions on the data, we prove that the solution set of such implicit obstacle problem is nonempty (so there is at least one solution) and weakly compact. The proof of our main result uses the Kakutani-Ky Fan fixed point theorem for multivalued operators along with the theory of nonsmooth analysis and variational methods for pseudomonotone operators. Copyright © 2021, The Authors. All rights reserved.

关键词： Fixed point arithmetic

Sigma-pi neural unit: Switching circuit simulation 19

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Sigma-pi neural unit: Switching circuit simulation

2019 International Conference on Artificial Intelligence, Information processing and Cloud Computing, AIIPCC 2019

作者： Potapov, Alexander A. Zhang, Wei Feng, Tianhua Rekhviashvili, Sergo V.A. Kotel’nikov Institute of Radio Engineering and Electronics Russian Academy of Sciences Moscow Russia JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China JNU-IREE Joint Laboratory of Fractal Method and Signal Processing Department of Electronic Engineering College of Information Science and Technology Jinan University Guangzhou China Institute of Applied Mathematics and Automation KBSC Russian Academy of Sciences Nalchik Russia

ISBN: (纸本)9781450376334

Electrical circuit to control power is designed for TTL IC sigma-pi neural units. The designed unit is implementable as an integral circuit. The unit representation features are considered in a given circuitry basis. A neural circuit simulation is performed through the PSpice interpreter. The properties of neural networks are considered. The prospects for creating multi-bit processors based on sigma-pi neural units are discussed. © 2019 Association for Computing Machinery.

关键词： Neural networks

Local Patch Network with Global Attention for Infrared Small Target Detection

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arXiv 2021年

作者： Chen, Fang Gao, Chenqiang Liu, Fangcen Zhao, Yue Zhou, Yuxi Meng, Deyu Zuo, Wangmeng School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications Chongqing400065 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa999078 China School of Mathematics and Statistics Xi’an Jiaotong University Shanxi Xi’an710049 China School of Computer Science and Technology Harbin Institute of Techonlogy Heilongjiang Harbin47822 China

Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and achieved noteworthy effects. Following general object segmentation methods, existing deep learning methods usually processed the image from the global view. However, the imaging locality of small targets and extreme class-imbalance between the target and background pixels were not well-considered by these deep learning methods, which causes the low-efficiency on training and high-dependence on numerous data. A local patch network (LPNet) with global attention is proposed in this paper to detect small targets by jointly considering the global and local properties of infrared small target images. From the global view, a supervised attention module trained by the small target spread map is proposed to suppress most background pixels irrelevant with small target features. From the local view, local patches are split from global features and share the same convolution weights with each other in a patch net. By leveraging both the global and local properties, the data-driven framework proposed in this paper has fused multi-scale features for small target detection. Extensive synthetic and real data experiments show that the proposed method achieves the state-of-the-art performance compared with existing both conventional and deep learning methods. © 2021, CC BY.

关键词： Infrared imaging